http://lcni-3.uoregon.edu/phenowiki/api.php?action=feedcontributions&user=Elizac&feedformat=atomPheno Wiki - User contributions [en]2024-03-28T22:35:41ZUser contributionsMediaWiki 1.26.2http://lcni-3.uoregon.edu/phenowiki/index.php?title=CNP_Stop_Signal&diff=8499CNP Stop Signal2015-04-24T22:17:23Z<p>Elizac: </p>
<hr />
<div>go back to [[HTAC]]<br />
<br />
<br />
=== Basic Task Description ===<br />
<br />
The Stop-signal task is a widely used measure of response inhibition and is based on a horse-race model of stopping, which assumes that independent go and stop processes race against one another to determine whether a response is executed or inhibited (Logan and Cowan, 1984; Logan, 1994) (though the independence assumption can be relaxed (Boucher et al., 2007). The primary dependent variable of the task, '''Stop-signal reaction time (SSRT)''', provides an individualized measure of inhibitory control. In this task, participants are presented with a series of Go stimuli to which they are instructed to respond quickly. This speeded reaction time task establishes a prepotency to respond. On a subset of trials, the Go stimulus is followed, after a variable delay, by a stop-signal, to which participants are instructed to inhibit their response. The onset of a the stop signal is varied and depends on the participant’s performance, such that it is decreased after a previous failure to inhibit and increased after a previous inhibition. This one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, and the horse-race model allows for the estimation of stop-signal reaction time (SSRT), an individualized measure of a participant’s inhibitory ability that controls for difficulty level.<br />
<br />
=== Task Procedure ===<br />
For general testing procedure, please refer to LA2K General Testing Procedure [here?].<br />
<br />
In this version of the Stop-signal task, participants were shown a series of go stimuli (“X” or “O”) in the center of the screen and were told to press the left arrow button on the keyboard when they saw an “X” and to press the right arrow button on the keyboard when they saw an “O” (Go trials). On a subset of trials, a stop-signal (a 500 Hz tone presented through headphones) was presented a short delay after the go stimulus appeared and lasted for 250 ms (Stop trials). Participants were instructed to respond as quickly and accurately as possible on all trials, but to withhold their response on Stop trials (on trials with the tone). Participants were instructed that stopping and going were equally important. <br />
<br />
On Stop trials, the delay of the onset of the stop-signal, or stop-signal delay (SSD), was varied, such that it was increased after the participant successfully inhibited in response to a stop-signal (making the next stop trial more difficult), and decreased after the participant failed to inhibit in response to a stop-signal (making the next stop trial less difficult). Each SSD increase or decrease was in 50 ms intervals. The SSD values were drawn from two interleaved staircases (or ladders) per block, resulting in 16 trials from each staircase for a total of 32 Stop trials per block. SSD values started at 200 and 300 ms for ladders 1 and 2, respectively, in the practice block. At the end of the practice block, the last SSD time from each staircase was then carried over to be the initial SSD for the first block (in the case of multiple practice blocks, the SSD starting values returned to 200 and 300 ms at the start of each practice block); at the end of the first block, the last SSD time from each staircase was carried over to be the initial SSD for the second block. This one-up/one-down tracking procedure ensured that subjects successfully inhibited on approximately 50% of inhibition trials. Also as a result, difficulty level is individualized across subjects and both behavioral performance and numbers of successful stop trials are equated across subjects.<br />
<br />
All participants received training on the Stop-signal task in the form of (at least) one practice block and two experiment blocks. The first practice block contained 22 Go trials and 10 stop trials presented randomly. Subjects were required to score over 20% accuracy on Stop practice trials and have average Go RT less than 750 ms in order to continue. Each experiment block contained 128 trials per block, 96 of which were Go trials and 32 of which were Stop trials (16 from Ladder 1, and 16 from Ladder 2), each presented randomly. Participants completed 2 successive blocks for a total of 256 trials. <br />
<br />
All trials started with a 500 ms fixation cross in the center of the screen and included a 1000 ms fixed response interval. Subjects were allowed to respond at the start of stimulus presentation until the end of the 1000 ms fixed response interval. Each trial was separated by a fixed 100 ms delay.<br />
<br />
Task presentation was administered via ePrime software. On each trial, the screen was black with white characters. The letters were written in Courier New font type (ePrime 26 size).<br />
<br />
=== Task Structure Detail ===<br />
''' This is what we had worked on before, but could use updating. We'd like to capture a schema that can handle each of the tasks in the CNP, so please think general when editing -fws'''<br />
<br />
* Task Structure ''(please given an overview of the task procedures here [i.e., overall design, block, trial, and within-trial event structure and timing])''<br />
** Participants performed at least one practice block and two experimental blocks<br />
*** Each experiment block contained 128 trials per block<br />
**** 96 Go trials <br />
**** 32 Stop trials <br />
***** 16 Stop trials from Ladder 1<br />
***** 16 Stop trials from Ladder 2 <br />
**** Participants completed 2 blocks for a total of 256 trials<br />
** Timing:<br />
*** All trials started with a 500 ms fixation cross in the center of the screen <br />
*** Each trial had a 1000 ms fixed response interval<br />
**** The onset of Go stimuli was the beginning of each trial’s 1000 ms fixed response interval<br />
**** The onset of Stop stimuli from two SSD ladders on a subset of trials (Stop trials) was variable<br />
***** SSD values for each of the two staircases started at 200 and 300 ms for ladder 1 and 2, respectively, in the practice block<br />
***** The last SSD time from each staircase in the practice block was carried over to be the initial SSD for the first block<br />
***** The last SSD time from each staircase in the first block was carried over to be the initial SSD for the second block<br />
**** Mean SSD ranged from approximately 65-650 ms across first 1000 participants (from 256 total trials)<br />
*** Each trial was separated by a fixed 100 ms delay<br />
<br />
* Stimulus Characteristics<br />
**sensory modality ''(e.g., visual, auditory, somatosensory, gustatory, olfactory)'': visual and auditory<br />
**presentation modality ''(e.g., human examiner, paper, computer display, headphones, speaker)'': computer display, headphones<br />
** Stimuli Descriptions:<br />
*** Go trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval<br />
*** Stop trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval and a stop-signal (500Hz tone), which was presented a variable delay after the onset of the go stimulus and lasted for 250 ms<br />
*** Stimuli were in ePrime (size setting 26), presented in white text on black background<br />
<br />
* Response Characteristics<br />
**responses required - Left arrow key press, Right arrow key press, or inhibition of key press<br />
***effector modality ''(e.g., vocal, manual, pedal)'': Manual response and response inhibition<br />
***functional modality ''(e.g., words, drawing, writing, keypress, movement)'': keypress and response inhibition<br />
**response options ''(e.g., yes/no, go/no-go, forced choice, multiple choice [specify n of options], free response)''- Go/No-Go<br />
**response collection ''(e.g., examiner notes, keyboard, keypad, mouse, voice key, button press)''- Keyboard button press<br />
**timing- see above<br />
<br />
=== Task Schematic ===<br />
<br />
Schemtic of the Stop-signal task.<br />
<br />
[[File:SST_Figure_2.png]]<br />
<br />
<br />
Schematic of the horse-race model of stopping and estimation of Stop-signal reaction time (SSRT).<br />
<br />
[[File:SSRT.png]]<br />
<br />
=== Task Parameters Table ===<br />
<br />
[[File:TaskParamTable.png]]<br />
<br />
=== Stimuli ===<br />
Go stimuli consisted of the letters “X” or “O”, which were presented in 26 size white Courier New font type in the center of a black screen. <br />
<br />
A stop stimulus was a 500 Hz tone presented through headphones, which was presented a short delay after the go stimulus appeared and lasted for 250 ms.<br />
<br />
A fixation stimulus was a fixation cross, which was presented in 26 size white Courier New font type in the center of a black screen.<br />
<br />
=== Dependent Variables ===<br />
The primary dependent variable is the Stop-signal reaction time (SSRT), which provides an individualized measure of inhibitory control. The use of a one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, which does not require an assumption of 50% inhibition, and allows us to use the quantile method to estimate SSRT (following Band et al., 2003). <br />
<br />
Other dependent variables that may be of interest include the mean RT on Go trials and the standard deviation of RT on Go trials. <br />
<br />
In contrast to other measures of response inhibition, such as the Go/No-Go task, percent inhibition is not a primary DV for the Stop-signal task, as this task is designed to ensure that participants inhibit on approximately 50% of all Stop trials. <br />
<br />
Additional summary measures can be used to screen outliers (see below). <br />
<br />
Table of all available variables.<br />
<br />
[[File:SST_Variables_Table.png]]<br />
<br />
=== Rules for Exclusion – Raw and Derived Variables ===<br />
1. If the number of trials is more or less than what is expected for each block (N = 128), that subject should be flagged for exclusion. If the inconsistency cannot be resolved, they should be excluded. <br />
<br />
2. If any of the derived variables (those listed in variables Table above) are missing, for either one or both testing blocks, that subject should be flagged for exclusion. <br />
=== Cleaning Rules – Derived Variables ===<br />
If any of the derived variables (those listed in variables Table above) are missing, for either one or both testing blocks, that participants should be flagged for exclusion. <br />
<br />
There are several decisions to make when estimating SSRT from more than one block of Stop-signal task performance data, including whether to average across all available sessions or to use the last session run (based on the assumption that participants are closest to their 50% inhibition point at the end of a session); whether to use all trials of each session or the last half only (again based on the assumption that participants stabilize inhibitory performance near the end of a session); and whether to use data from all participants that completed the task regardless of performance, or to use either conservative or lenient criteria to exclude outliers, so as to avoid violating assumptions underlying the race-model of stopping. These questions have been systematically assessed in an independent dataset, which was randomly split into halves in order to evaluate reliability and repeatability of SSRT estimates derived following multiple approaches to data cleaning (Congdon et al., 2012). Measures of reliability, including intraclass correlation coeffcients (ICC) and within-subject variability, and the resulting sample size, were used as indicators to evaluate the different strategies to data cleaning. <br />
<br />
Our results suggest that an approach that uses the average of all available Stop-signal blocks, all trials of each block, and excludes outliers based on predetermined lenient criteria (defined below) yields reliable SSRT estimates and low within-subject variability, while not excluding too many participants from the total dataset. Specifically, this approach resulted in an ICC value of 0.79 and a within-subject variability estimate of 25.42 ms, while only excluding 7 (out of 184) participants. Critically, this approach also retains a broad distribution of SSRT values. <br />
<br />
Based on these analyses, [[the following cleaning rules are suggested]]: <br />
* Use all trials from each testing block<br />
* Average across both testing blocks for final summary scores<br />
* Exclude outliers that meet the following lenient criteria:<br />
** Percent inhibition on Stop trials less than 25% or greater than 75%<br />
** Percent correct responding on Go trials less than 60%<br />
** Percent incorrect Go trials greater than 10%<br />
** SSRT estimate that is negative or less than 50 ms<br />
<br />
=== Code/Algorithms ===<br />
<br />
Scoring of behavioral data proceeded as follows. <br />
<br />
The mean, median and standard deviation of reaction time on Go trials were calculated only for Go trials in which participants correctly responded. Stop successful trials included only Stop trials on which participants successfully inhibited a response, and Stop unsuccessful trials included only Stop trials on which participants responded. Average stop-signal delay (SSD) was calculated from SSD values across both ladders. SSRT was estimated using the quantile method, which does not require an assumption of 50% inhibition (Band et al., 2003). Briefly, to calculate SSRT following the quantile method, all correct RTs from Go trials were arranged in ascending order. The proportion of failed inhibition, which is the proportion of Stop trials in which the participants responded, was calculated across both ladders. The quantileRT was determined by finding the RT corresponding to the proportion of failed inhibition. The average stop-signal delay (across both ladders) was then subtracted from the quantileRT in order to calculate SSRT (Band et al., 2003). <br />
<br />
Scores are calculated from each block separately and then averaged to provide summary scores for the overall session. It is recommended that one use the final measures based on the overall session, as they provide more stable estimates of SSRT (Band et al., 2003; Congdon et al., 2012). <br />
<br />
------<br />
<br />
Make 2 Filters: Procedure[Block] and Running[Block] and Procedure[Trial]<br />
<br />
First filter to only include trials where Procedure[Block] = “StopProc” (these are the real trials)<br />
<br />
Analyze the remaining trials in 2 different sets: Those with Running[Block] = “Block1” and those with Running[Block] = “Block2”<br />
<br />
For each of those two sets (Block1 and Block2) do the following steps:<br />
<br />
{<br />
<br />
* '''Block1_Direction_Errors''' = Number of trials where (Procedure[Trial] = "StGTrial" AND Go.RT> 0 AND Go.ACC= 0) OR (Procedure[Trial] = "StGTrial" AND Go.RT = 0 AND Blank.Resp != CorrectAnswer)<br />
* '''Block1_Percent_Go_Response''' = [Number of trials where Procedure[Trial] = "StGTrial" AND (Go.ACC=1 OR Blank.ACC=1)] / Number of trials where Procedure[Trial] = "StGTrial" <br />
<br />
For the next calculation, you need the following values:<br />
<br />
* Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND ('''GoAcc = 1 AND Go.RT > 0''')<br />
* Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND ('''GoACC = 0 AND Go.RT = 0 AND Blank.ACC = 1''')<br />
<br />
* '''Block1_Mean_RT''' = Mean of all values that you just got from Go.RT and Blank.RT + 1000<br />
* '''Block1_Median_RT''' = Median of all values that you just got from Go.RT and Blank.RT + 1000<br />
* '''Block1_StDev_RT''' = Standard deviation of all values that you just got from Go.RT and Blank.RT + 1000<br />
<br />
For the next step, you need to get have these numbers:<br />
<br />
* GoDur - 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2S.RT=0 and Blanks.RT=0)<br />
* GoDur + 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT!=0 or Inhs.RT!=0 or Go2S.RT!=0 or Blanks.RT!=0)<br />
* GoDur2 - 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)<br />
* GoDur2 + 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT!=0 or Inhs2.RT!=0 or Go2s2.RT!=0 or Blanks.RT!=0)<br />
<br />
* '''Block1_Ladder1Mean''' = Mean of all GoDur values from the previous statements (there should be 16 total)<br />
* '''Block1_Ladder2Mean''' = Mean of all GoDur2 values from the previous statements (there should be 16 total)<br />
* '''Block1_SSD50''' = Mean of ALL the values you just got from the previous statements (there should be 32 total values that you're taking the mean of)<br />
<br />
<br />
* '''Block1_PctInhib_Ladder1''' = [Number of Trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2s.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial")<br />
* '''Block1_PctInhib_Ladder2''' = [Number of Trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial2)<br />
* '''Block1_Percent_Inhib''' = Mean of Block1_PctInhib_Ladder1 and Block1_PctInhib_Ladder2<br />
<br />
* '''Block1_Quantile_Value''' = 1 - Block1_Percent_Inhib<br />
<br />
* '''Block1_SSRT''' = Block1_Median_RT - Block1_SSD50<br />
<br />
* CorrectGoRT on StopProc trials: As a reminder, these are Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND (GoAcc = 1 AND Go.RT > 0), and Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND (GoACC = 0 AND Go.RT = 0 AND Blank.ACC = 1). Use these CorrectGoRT on StopProc trials to calculate Block1_Quant_RT:<br />
<br />
* '''Block1_Quant_RT''' = Quantile calculation of all the values from Go.RT and Blank.RT + 1000 (the values used in the calculations where you calculated Block1_Mean_RT, etc), taken using Quantile_Value. So if Quantile_Value = .85, you'd take the .85-quantile of the distribution of values from Go.RT and (Blank.RT + 1000)<br />
<br />
* '''Block1_SSRT_Quant''' = Block1_Quant_RT - Block1_SSD50<br />
<br />
}<br />
<br />
Then, do all those same calculations that were in the braces, but using the data points that were from Running[Block] = “Block2” (and save the variables as Block2_whatever, rather than Block1)<br />
<br />
Then average the above values together to get a measure of overall task performance: <br />
<br />
All the values that we need outputted are in bold. See also Variable List. <br />
<br />
<br />
'''If you have any questions about the above scoring or notice any errors, please e-mail Eliza at econgdon@ucla.edu.'''<br />
<br />
-------<br />
<br />
The above text instructions can be adapted for any program. These instructions are currently implemented in Stone’s scoring scripts for the scoring of LA2K variables. These steps have been implemented in separate Matlab scripts by Eliza and have been used to verify Stone’s scoring scripts. These steps have also been used to manually score the data and verify Stone’s scoring scripts multiple times (finalized April 2010). <br />
<br />
''Note that one thing that may differ in the scoring script between programs is quantile calculation. For example, the quantile function in Matlab differs slightly from the percentile function in Excel. Stone’s scoring method may differ by a tiny fraction from the Matlab quantile function; however, this difference is tiny, and as long as the same scoring script is applied to all subjects (as it is in LA2K), then it does not matter. All agreed April 2010 on this issue.<br />
''<br />
<br />
''Note also to users interested in trial-by-trial data. Do not use the GoDur and GoDur2 values as SSD values. These are placeholders only and do not reflect actual SSD values, which tracked subject’s performance. Instead, you must apply the following code to the GoDur and GoDur2 values in order to get accurate SSD_1 and SSD_2 values, respectively: <br />
* GoDur - 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2S.RT=0 and Blanks.RT=0)<br />
* GoDur + 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT!=0 or Inhs.RT!=0 or Go2S.RT!=0 or Blanks.RT!=0)<br />
* GoDur2 - 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)<br />
* GoDur2 + 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT!=0 or Inhs2.RT!=0 or Go2s2.RT!=0 or Blanks.RT!=0)<br />
''<br />
History of Checking Scoring:<br />
* October 2009: Eliza confirmed Stone’s scripts by manually scoring data (completed 10/19/2009)<br />
<br />
* January 2010: Another group (Nicole McLaughlin at Butler Hospital) that was sent the scoring scripts identified a scoring error. <br />
** Eliza and Stone worked together to resolve the problem. Stone fixed that error in the script on 1/29/2010. <br />
** Eliza and Stone clarified an issue about the scoring of Go trial (direction) errors and matched scores between UCLA and Butler (March 2010). <br />
** Final questions about differences in quantile calculation were resolved in April. Eliza and Stone calculated all scores on the same data and met to confirm scoring scripts. Everything agreed and finalized 4/21/10. <br />
<br />
* Eliza confirmed SSD starting values, and updated trial-by-trial data, on 8/13/11.<br />
<br />
=== Data Distributions ===<br />
<br />
These are based on Congdon Query 80 (7/27/11)-Derived Data; Query 82 (7/27/11)-Trial-by-Trial Data; and updated with Congdon Query (1/17/12).<br />
<br />
[[File:1_Results.png]]<br />
<br />
[[File:2_Results.png]]<br />
<br />
[[File:3_Results.png]]<br />
<br />
[[File:4_Results.png]]<br />
<br />
<br />
Updated 8/2/12 by EC: <br/><br />
The following subjects should be excluded for the following reasons: <br/><br />
* Missing key derived variables because of poor performance: 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198. <br />
* Percent Inhibition outside the allowable range: 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076<br />
* Too many Go errors: 10176<br />
* In addition to those that just have missing SST data.<br />
<br />
=== References ===<br />
Band, G. P., van der Molen, M. W., and Logan, G. D. (2003). Horse-race model simulations of the stop-signal procedure. ''Acta Psychol, 112,'' 105-142.<br />
<br />
Boucher, L., Palmeri, T. J., Logan, G. D., and Schall, J. D. (2007). Inhibitory control in mind and brain: An interactive race model of countermanding saccades. ''Psychol Rev, 114 (2),'' 376-97. <br />
<br />
Congdon, E., Mumford, J., Cohen, J., Galvan, A., Canli, T., & Poldrack, R. A. (2012). Measurement and reliability of response inhibition. ''Frontiers in Psychology, 3 (37),'' doi: 10.3389/fpsyg.2012.00037.<br />
<br />
Logan, G. D., and Cowan, W. B. (1984). On the ability to inhibit through and action: a theory of an act of control. ''Psychol Rev, 91,'' 295-327.<br />
<br />
Logan, G. D. (1994). On the ability to inhibit thought and action: A users’ guide to the stop signal paradigm. In: Dagenbach, D., Carr, T. H. (Eds.), Inhibitory Processes in Attention, Memory and Language. Academic Press, San Diego, pp. 189-239.<br />
<br />
=== Methods Synopsis ===<br />
<br />
Participants completed a tracking Stop-signal task, which is based on a horse-race model of response inhibition (Logan & Cowan, 1984; Logan, 1994). Participants were presented with a series of Go stimuli (“X” or “O”) to which they were instructed to respond quickly (with a left or right button press, respectively) and, on a subset of trials (25%), the Go stimulus was followed, after a variable delay, by a stop-signal (a 500 Hz tone presented through headphones). Participants were instructed to inhibit responses on trials in which the stop-signal appeared, and that correctly responding and inhibiting were equally important. After completing at least one practice block of 32 trials (22 of which were Go trials), participants completed two blocks of 128 trials each, for a total of 256 trials.<br />
<br />
On Stop trials, the delay of the onset of the stop-signal, or stop-signal delay (SSD), was varied, such that it was increased after the participant successfully inhibited in response to a stop-signal (making the next stop trial more difficult), and decreased after the participant failed to inhibit in response to a stop-signal (making the next stop trial less difficult). Each SSD increase or decrease was in 50 ms intervals. The SSD values were drawn from two interleaved staircases (or ladders) per block, resulting in 16 trials from each staircase for a total of 32 Stop trials per block. SSD values for ladders 1 and 2 began with SSD values of 200 and 300 ms, respectively, in the practice block. At the end of the practice and first blocks, the last SSD times from each staircase were then carried over to be the initial SSD values for the first and second blocks, respectively. <br />
<br />
All trials started with a 500 ms fixation cross in the center of the screen and included a 1000 ms fixed response interval. Subjects were allowed to respond at the start of stimulus presentation until the end of the 1000 ms fixed response interval. Each trial was separated by a fixed 100 ms delay. Task presentation was administered via ePrime software.<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=CNP_Stop_Signal&diff=8498CNP Stop Signal2015-04-24T22:16:32Z<p>Elizac: </p>
<hr />
<div>go back to [[HTAC]]<br />
<br />
<br />
=== Basic Task Description ===<br />
<br />
The Stop-signal task is a widely used measure of response inhibition and is based on a horse-race model of stopping, which assumes that independent go and stop processes race against one another to determine whether a response is executed or inhibited (Logan and Cowan, 1984; Logan, 1994) (though the independence assumption can be relaxed (Boucher et al., 2007). The primary dependent variable of the task, '''Stop-signal reaction time (SSRT)''', provides an individualized measure of inhibitory control. In this task, participants are presented with a series of Go stimuli to which they are instructed to respond quickly. This speeded reaction time task establishes a prepotency to respond. On a subset of trials, the Go stimulus is followed, after a variable delay, by a stop-signal, to which participants are instructed to inhibit their response. The onset of a the stop signal is varied and depends on the participant’s performance, such that it is decreased after a previous failure to inhibit and increased after a previous inhibition. This one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, and the horse-race model allows for the estimation of stop-signal reaction time (SSRT), an individualized measure of a participant’s inhibitory ability that controls for difficulty level.<br />
<br />
=== Task Procedure ===<br />
For general testing procedure, please refer to LA2K General Testing Procedure [here?].<br />
<br />
In this version of the Stop-signal task, participants were shown a series of go stimuli (“X” or “O”) in the center of the screen and were told to press the left arrow button on the keyboard when they saw an “X” and to press the right arrow button on the keyboard when they saw an “O” (Go trials). On a subset of trials, a stop-signal (a 500 Hz tone presented through headphones) was presented a short delay after the go stimulus appeared and lasted for 250 ms (Stop trials). Participants were instructed to respond as quickly and accurately as possible on all trials, but to withhold their response on Stop trials (on trials with the tone). Participants were instructed that stopping and going were equally important. <br />
<br />
On Stop trials, the delay of the onset of the stop-signal, or stop-signal delay (SSD), was varied, such that it was increased after the participant successfully inhibited in response to a stop-signal (making the next stop trial more difficult), and decreased after the participant failed to inhibit in response to a stop-signal (making the next stop trial less difficult). Each SSD increase or decrease was in 50 ms intervals. The SSD values were drawn from two interleaved staircases (or ladders) per block, resulting in 16 trials from each staircase for a total of 32 Stop trials per block. SSD values started at 200 and 300 ms for ladders 1 and 2, respectively, in the practice block. At the end of the practice block, the last SSD time from each staircase was then carried over to be the initial SSD for the first block (in the case of multiple practice blocks, the SSD starting values returned to 200 and 300 ms at the start of each practice block); at the end of the first block, the last SSD time from each staircase was carried over to be the initial SSD for the second block. This one-up/one-down tracking procedure ensured that subjects successfully inhibited on approximately 50% of inhibition trials. Also as a result, difficulty level is individualized across subjects and both behavioral performance and numbers of successful stop trials are equated across subjects.<br />
<br />
All participants received training on the Stop-signal task in the form of (at least) one practice block and two experiment blocks. The first practice block contained 22 Go trials and 10 stop trials presented randomly. Subjects were required to score over 20% accuracy on Stop practice trials and have average Go RT less than 750 ms in order to continue. Each experiment block contained 128 trials per block, 96 of which were Go trials and 32 of which were Stop trials (16 from Ladder 1, and 16 from Ladder 2), each presented randomly. Participants completed 2 successive blocks for a total of 256 trials. <br />
<br />
All trials started with a 500 ms fixation cross in the center of the screen and included a 1000 ms fixed response interval. Subjects were allowed to respond at the start of stimulus presentation until the end of the 1000 ms fixed response interval. Each trial was separated by a fixed 100 ms delay.<br />
<br />
Task presentation was administered via ePrime software. On each trial, the screen was black with white characters. The letters were written in Courier New font type (ePrime 26 size).<br />
<br />
=== Task Structure Detail ===<br />
''' This is what we had worked on before, but could use updating. We'd like to capture a schema that can handle each of the tasks in the CNP, so please think general when editing -fws'''<br />
<br />
* Task Structure ''(please given an overview of the task procedures here [i.e., overall design, block, trial, and within-trial event structure and timing])''<br />
** Participants performed at least one practice block and two experimental blocks<br />
*** Each experiment block contained 128 trials per block<br />
**** 96 Go trials <br />
**** 32 Stop trials <br />
***** 16 Stop trials from Ladder 1<br />
***** 16 Stop trials from Ladder 2 <br />
**** Participants completed 2 blocks for a total of 256 trials<br />
** Timing:<br />
*** All trials started with a 500 ms fixation cross in the center of the screen <br />
*** Each trial had a 1000 ms fixed response interval<br />
**** The onset of Go stimuli was the beginning of each trial’s 1000 ms fixed response interval<br />
**** The onset of Stop stimuli from two SSD ladders on a subset of trials (Stop trials) was variable<br />
***** SSD values for each of the two staircases started at 200 and 300 ms for ladder 1 and 2, respectively, in the practice block<br />
***** The last SSD time from each staircase in the practice block was carried over to be the initial SSD for the first block<br />
***** The last SSD time from each staircase in the first block was carried over to be the initial SSD for the second block<br />
**** Mean SSD ranged from approximately 65-650 ms across first 1000 participants (from 256 total trials)<br />
*** Each trial was separated by a fixed 100 ms delay<br />
<br />
* Stimulus Characteristics<br />
**sensory modality ''(e.g., visual, auditory, somatosensory, gustatory, olfactory)'': visual and auditory<br />
**presentation modality ''(e.g., human examiner, paper, computer display, headphones, speaker)'': computer display, headphones<br />
** Stimuli Descriptions:<br />
*** Go trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval<br />
*** Stop trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval and a stop-signal (500Hz tone), which was presented a variable delay after the onset of the go stimulus and lasted for 250 ms<br />
*** Stimuli were in ePrime (size setting 26), presented in white text on black background<br />
<br />
* Response Characteristics<br />
**responses required - Left arrow key press, Right arrow key press, or inhibition of key press<br />
***effector modality ''(e.g., vocal, manual, pedal)'': Manual response and response inhibition<br />
***functional modality ''(e.g., words, drawing, writing, keypress, movement)'': keypress and response inhibition<br />
**response options ''(e.g., yes/no, go/no-go, forced choice, multiple choice [specify n of options], free response)''- Go/No-Go<br />
**response collection ''(e.g., examiner notes, keyboard, keypad, mouse, voice key, button press)''- Keyboard button press<br />
**timing- see above<br />
<br />
=== Task Schematic ===<br />
<br />
Schemtic of the Stop-signal task.<br />
<br />
[[File:SST_Figure_2.png]]<br />
<br />
<br />
Schematic of the horse-race model of stopping and estimation of Stop-signal reaction time (SSRT).<br />
<br />
[[File:SSRT.png]]<br />
<br />
=== Task Parameters Table ===<br />
<br />
[[File:TaskParamTable.png]]<br />
<br />
=== Stimuli ===<br />
Go stimuli consisted of the letters “X” or “O”, which were presented in 26 size white Courier New font type in the center of a black screen. <br />
<br />
A stop stimulus was a 500 Hz tone presented through headphones, which was presented a short delay after the go stimulus appeared and lasted for 250 ms.<br />
<br />
A fixation stimulus was a fixation cross, which was presented in 26 size white Courier New font type in the center of a black screen.<br />
<br />
=== Dependent Variables ===<br />
The primary dependent variable is the Stop-signal reaction time (SSRT), which provides an individualized measure of inhibitory control. The use of a one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, which does not require an assumption of 50% inhibition, and allows us to use the quantile method to estimate SSRT (following Band et al., 2003). <br />
<br />
Other dependent variables that may be of interest include the mean RT on Go trials and the standard deviation of RT on Go trials. <br />
<br />
In contrast to other measures of response inhibition, such as the Go/No-Go task, percent inhibition is not a primary DV for the Stop-signal task, as this task is designed to ensure that participants inhibit on approximately 50% of all Stop trials. <br />
<br />
Additional summary measures can be used to screen outliers (see below). <br />
<br />
Table of all available variables.<br />
<br />
[[File:SST_Variables_Table.png]]<br />
<br />
=== Rules for Exclusion – Raw and Derived Variables ===<br />
1. If the number of trials is more or less than what is expected for each block (N = 128), that subject should be flagged for exclusion. If the inconsistency cannot be resolved, they should be excluded. <br />
<br />
2. If any of the derived variables (those listed in variables Table above) are missing, for either one or both testing blocks, that subject should be flagged for exclusion. <br />
=== Cleaning Rules – Derived Variables ===<br />
If any of the derived variables (those listed in variables Table above) are missing, for either one or both testing blocks, that participants should be flagged for exclusion. <br />
<br />
There are several decisions to make when estimating SSRT from more than one block of Stop-signal task performance data, including whether to average across all available sessions or to use the last session run (based on the assumption that participants are closest to their 50% inhibition point at the end of a session); whether to use all trials of each session or the last half only (again based on the assumption that participants stabilize inhibitory performance near the end of a session); and whether to use data from all participants that completed the task regardless of performance, or to use either conservative or lenient criteria to exclude outliers, so as to avoid violating assumptions underlying the race-model of stopping. These questions have been systematically assessed in an independent dataset, which was randomly split into halves in order to evaluate reliability and repeatability of SSRT estimates derived following multiple approaches to data cleaning (Congdon et al., 2012). Measures of reliability, including intraclass correlation coeffcients (ICC) and within-subject variability, and the resulting sample size, were used as indicators to evaluate the different strategies to data cleaning. <br />
<br />
Our results suggest that an approach that uses the average of all available Stop-signal blocks, all trials of each block, and excludes outliers based on predetermined lenient criteria (defined below) yields reliable SSRT estimates and low within-subject variability, while not excluding too many participants from the total dataset. Specifically, this approach resulted in an ICC value of 0.79 and a within-subject variability estimate of 25.42 ms, while only excluding 7 (out of 184) participants. Critically, this approach also retains a broad distribution of SSRT values. <br />
<br />
Based on these analyses, [[the following cleaning rules are suggested]]: <br />
* Use all trials from each testing block<br />
* Average across both testing blocks for final summary scores<br />
* Exclude outliers that meet the following lenient criteria:<br />
** Percent inhibition on Stop trials less than 25% or greater than 75%<br />
** Percent correct responding on Go trials less than 60%<br />
** Percent incorrect Go trials greater than 10%<br />
** SSRT estimate that is negative or less than 50 ms<br />
<br />
=== Code/Algorithms ===<br />
<br />
Scoring of behavioral data proceeded as follows. <br />
<br />
The mean, median and standard deviation of reaction time on Go trials were calculated only for Go trials in which participants correctly responded. Stop successful trials included only Stop trials on which participants successfully inhibited a response, and Stop unsuccessful trials included only Stop trials on which participants responded. Average stop-signal delay (SSD) was calculated from SSD values across both ladders. SSRT was estimated using the quantile method, which does not require an assumption of 50% inhibition (Band et al., 2003). Briefly, to calculate SSRT following the quantile method, all correct RTs from Go trials were arranged in ascending order. The proportion of failed inhibition, which is the proportion of Stop trials in which the participants responded, was calculated across both ladders. The quantileRT was determined by finding the RT corresponding to the proportion of failed inhibition. The average stop-signal delay (across both ladders) was then subtracted from the quantileRT in order to calculate SSRT (Band et al., 2003). <br />
<br />
Scores are calculated from each block separately and then averaged to provide summary scores for the overall session. It is recommended that one use the final measures based on the overall session, as they provide more stable estimates of SSRT (Band et al., 2003; Congdon et al., 2012). <br />
<br />
------<br />
<br />
Make 2 Filters: Procedure[Block] and Running[Block] and Procedure[Trial]<br />
<br />
First filter to only include trials where Procedure[Block] = “StopProc” (these are the real trials)<br />
<br />
Analyze the remaining trials in 2 different sets: Those with Running[Block] = “Block1” and those with Running[Block] = “Block2”<br />
<br />
For each of those two sets (Block1 and Block2) do the following steps:<br />
<br />
{<br />
<br />
* '''Block1_Direction_Errors''' = Number of trials where (Procedure[Trial] = "StGTrial" AND Go.RT> 0 AND Go.ACC= 0) OR (Procedure[Trial] = "StGTrial" AND Go.RT = 0 AND Blank.Resp != CorrectAnswer)<br />
* '''Block1_Percent_Go_Response''' = [Number of trials where Procedure[Trial] = "StGTrial" AND (Go.ACC=1 OR Blank.ACC=1)] / Number of trials where Procedure[Trial] = "StGTrial" <br />
<br />
For the next calculation, you need the following values:<br />
<br />
* Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND ('''GoAcc = 1 AND Go.RT > 0''')<br />
* Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND ('''GoACC = 0 AND Go.RT = 0 AND Blank.ACC = 1''')<br />
<br />
* '''Block1_Mean_RT''' = Mean of all values that you just got from Go.RT and Blank.RT + 1000<br />
* '''Block1_Median_RT''' = Median of all values that you just got from Go.RT and Blank.RT + 1000<br />
* '''Block1_StDev_RT''' = Standard deviation of all values that you just got from Go.RT and Blank.RT + 1000<br />
<br />
For the next step, you need to get have these numbers:<br />
<br />
* GoDur - 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2S.RT=0 and Blanks.RT=0)<br />
* GoDur + 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT!=0 or Inhs.RT!=0 or Go2S.RT!=0 or Blanks.RT!=0)<br />
* GoDur2 - 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)<br />
* GoDur2 + 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT!=0 or Inhs2.RT!=0 or Go2s2.RT!=0 or Blanks.RT!=0)<br />
<br />
* '''Block1_Ladder1Mean''' = Mean of all GoDur values from the previous statements (there should be 16 total)<br />
* '''Block1_Ladder2Mean''' = Mean of all GoDur2 values from the previous statements (there should be 16 total)<br />
* '''Block1_SSD50''' = Mean of ALL the values you just got from the previous statements (there should be 32 total values that you're taking the mean of)<br />
<br />
<br />
* '''Block1_PctInhib_Ladder1''' = [Number of Trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2s.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial")<br />
* '''Block1_PctInhib_Ladder2''' = [Number of Trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial2)<br />
* '''Block1_Percent_Inhib''' = Mean of Block1_PctInhib_Ladder1 and Block1_PctInhib_Ladder2<br />
<br />
* '''Block1_Quantile_Value''' = 1 - Block1_Percent_Inhib<br />
<br />
* '''Block1_SSRT''' = Block1_Median_RT - Block1_SSD50<br />
<br />
* CorrectGoRT on StopProc trials: As a reminder, these are Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND (GoAcc = 1 AND Go.RT > 0), and Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND (GoACC = 0 AND Go.RT = 0 AND Blank.ACC = 1). Use these CorrectGoRT on StopProc trials to calculate Block1_Quant_RT:<br />
<br />
* '''Block1_Quant_RT''' = Quantile calculation of all the values from Go.RT and Blank.RT + 1000 (the values used in the calculations where you calculated Block1_Mean_RT, etc), taken using Quantile_Value. So if Quantile_Value = .85, you'd take the .85-quantile of the distribution of values from Go.RT and (Blank.RT + 1000)<br />
<br />
* '''Block1_SSRT_Quant''' = Block1_Quant_RT - Block1_SSD50<br />
<br />
}<br />
<br />
Then, do all those same calculations that were in the braces, but using the data points that were from Running[Block] = “Block2” (and save the variables as Block2_whatever, rather than Block1)<br />
<br />
Then average the above values together to get a measure of overall task performance: <br />
<br />
All the values that we need outputted are in bold. See also Variable List. <br />
<br />
-------<br />
<br />
The above text instructions can be adapted for any program. These instructions are currently implemented in Stone’s scoring scripts for the scoring of LA2K variables. These steps have been implemented in separate Matlab scripts by Eliza and have been used to verify Stone’s scoring scripts. These steps have also been used to manually score the data and verify Stone’s scoring scripts multiple times (finalized April 2010). <br />
<br />
If you have any questions about the above scoring or notice any errors, please e-mail Eliza at econgdon@ucla.edu.<br />
<br />
''Note that one thing that may differ in the scoring script between programs is quantile calculation. For example, the quantile function in Matlab differs slightly from the percentile function in Excel. Stone’s scoring method may differ by a tiny fraction from the Matlab quantile function; however, this difference is tiny, and as long as the same scoring script is applied to all subjects (as it is in LA2K), then it does not matter. All agreed April 2010 on this issue.<br />
''<br />
<br />
''Note also to users interested in trial-by-trial data. Do not use the GoDur and GoDur2 values as SSD values. These are placeholders only and do not reflect actual SSD values, which tracked subject’s performance. Instead, you must apply the following code to the GoDur and GoDur2 values in order to get accurate SSD_1 and SSD_2 values, respectively: <br />
* GoDur - 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2S.RT=0 and Blanks.RT=0)<br />
* GoDur + 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT!=0 or Inhs.RT!=0 or Go2S.RT!=0 or Blanks.RT!=0)<br />
* GoDur2 - 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)<br />
* GoDur2 + 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT!=0 or Inhs2.RT!=0 or Go2s2.RT!=0 or Blanks.RT!=0)<br />
''<br />
History of Checking Scoring:<br />
* October 2009: Eliza confirmed Stone’s scripts by manually scoring data (completed 10/19/2009)<br />
<br />
* January 2010: Another group (Nicole McLaughlin at Butler Hospital) that was sent the scoring scripts identified a scoring error. <br />
** Eliza and Stone worked together to resolve the problem. Stone fixed that error in the script on 1/29/2010. <br />
** Eliza and Stone clarified an issue about the scoring of Go trial (direction) errors and matched scores between UCLA and Butler (March 2010). <br />
** Final questions about differences in quantile calculation were resolved in April. Eliza and Stone calculated all scores on the same data and met to confirm scoring scripts. Everything agreed and finalized 4/21/10. <br />
<br />
* Eliza confirmed SSD starting values, and updated trial-by-trial data, on 8/13/11.<br />
<br />
=== Data Distributions ===<br />
<br />
These are based on Congdon Query 80 (7/27/11)-Derived Data; Query 82 (7/27/11)-Trial-by-Trial Data; and updated with Congdon Query (1/17/12).<br />
<br />
[[File:1_Results.png]]<br />
<br />
[[File:2_Results.png]]<br />
<br />
[[File:3_Results.png]]<br />
<br />
[[File:4_Results.png]]<br />
<br />
<br />
Updated 8/2/12 by EC: <br/><br />
The following subjects should be excluded for the following reasons: <br/><br />
* Missing key derived variables because of poor performance: 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198. <br />
* Percent Inhibition outside the allowable range: 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076<br />
* Too many Go errors: 10176<br />
* In addition to those that just have missing SST data.<br />
<br />
=== References ===<br />
Band, G. P., van der Molen, M. W., and Logan, G. D. (2003). Horse-race model simulations of the stop-signal procedure. ''Acta Psychol, 112,'' 105-142.<br />
<br />
Boucher, L., Palmeri, T. J., Logan, G. D., and Schall, J. D. (2007). Inhibitory control in mind and brain: An interactive race model of countermanding saccades. ''Psychol Rev, 114 (2),'' 376-97. <br />
<br />
Congdon, E., Mumford, J., Cohen, J., Galvan, A., Canli, T., & Poldrack, R. A. (2012). Measurement and reliability of response inhibition. ''Frontiers in Psychology, 3 (37),'' doi: 10.3389/fpsyg.2012.00037.<br />
<br />
Logan, G. D., and Cowan, W. B. (1984). On the ability to inhibit through and action: a theory of an act of control. ''Psychol Rev, 91,'' 295-327.<br />
<br />
Logan, G. D. (1994). On the ability to inhibit thought and action: A users’ guide to the stop signal paradigm. In: Dagenbach, D., Carr, T. H. (Eds.), Inhibitory Processes in Attention, Memory and Language. Academic Press, San Diego, pp. 189-239.<br />
<br />
=== Methods Synopsis ===<br />
<br />
Participants completed a tracking Stop-signal task, which is based on a horse-race model of response inhibition (Logan & Cowan, 1984; Logan, 1994). Participants were presented with a series of Go stimuli (“X” or “O”) to which they were instructed to respond quickly (with a left or right button press, respectively) and, on a subset of trials (25%), the Go stimulus was followed, after a variable delay, by a stop-signal (a 500 Hz tone presented through headphones). Participants were instructed to inhibit responses on trials in which the stop-signal appeared, and that correctly responding and inhibiting were equally important. After completing at least one practice block of 32 trials (22 of which were Go trials), participants completed two blocks of 128 trials each, for a total of 256 trials.<br />
<br />
On Stop trials, the delay of the onset of the stop-signal, or stop-signal delay (SSD), was varied, such that it was increased after the participant successfully inhibited in response to a stop-signal (making the next stop trial more difficult), and decreased after the participant failed to inhibit in response to a stop-signal (making the next stop trial less difficult). Each SSD increase or decrease was in 50 ms intervals. The SSD values were drawn from two interleaved staircases (or ladders) per block, resulting in 16 trials from each staircase for a total of 32 Stop trials per block. SSD values for ladders 1 and 2 began with SSD values of 200 and 300 ms, respectively, in the practice block. At the end of the practice and first blocks, the last SSD times from each staircase were then carried over to be the initial SSD values for the first and second blocks, respectively. <br />
<br />
All trials started with a 500 ms fixation cross in the center of the screen and included a 1000 ms fixed response interval. Subjects were allowed to respond at the start of stimulus presentation until the end of the 1000 ms fixed response interval. Each trial was separated by a fixed 100 ms delay. Task presentation was administered via ePrime software.<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=CNP_Stop_Signal&diff=8497CNP Stop Signal2015-04-24T22:00:16Z<p>Elizac: </p>
<hr />
<div>go back to [[HTAC]]<br />
<br />
<br />
=== Basic Task Description ===<br />
<br />
The Stop-signal task is a widely used measure of response inhibition and is based on a horse-race model of stopping, which assumes that independent go and stop processes race against one another to determine whether a response is executed or inhibited (Logan and Cowan, 1984; Logan, 1994) (though the independence assumption can be relaxed (Boucher et al., 2007). The primary dependent variable of the task, '''Stop-signal reaction time (SSRT)''', provides an individualized measure of inhibitory control. In this task, participants are presented with a series of Go stimuli to which they are instructed to respond quickly. This speeded reaction time task establishes a prepotency to respond. On a subset of trials, the Go stimulus is followed, after a variable delay, by a stop-signal, to which participants are instructed to inhibit their response. The onset of a the stop signal is varied and depends on the participant’s performance, such that it is decreased after a previous failure to inhibit and increased after a previous inhibition. This one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, and the horse-race model allows for the estimation of stop-signal reaction time (SSRT), an individualized measure of a participant’s inhibitory ability that controls for difficulty level.<br />
<br />
=== Task Procedure ===<br />
For general testing procedure, please refer to LA2K General Testing Procedure [here?].<br />
<br />
In this version of the Stop-signal task, participants were shown a series of go stimuli (“X” or “O”) in the center of the screen and were told to press the left arrow button on the keyboard when they saw an “X” and to press the right arrow button on the keyboard when they saw an “O” (Go trials). On a subset of trials, a stop-signal (a 500 Hz tone presented through headphones) was presented a short delay after the go stimulus appeared and lasted for 250 ms (Stop trials). Participants were instructed to respond as quickly and accurately as possible on all trials, but to withhold their response on Stop trials (on trials with the tone). Participants were instructed that stopping and going were equally important. <br />
<br />
On Stop trials, the delay of the onset of the stop-signal, or stop-signal delay (SSD), was varied, such that it was increased after the participant successfully inhibited in response to a stop-signal (making the next stop trial more difficult), and decreased after the participant failed to inhibit in response to a stop-signal (making the next stop trial less difficult). Each SSD increase or decrease was in 50 ms intervals. The SSD values were drawn from two interleaved staircases (or ladders) per block, resulting in 16 trials from each staircase for a total of 32 Stop trials per block. SSD values started at 200 and 300 ms for ladders 1 and 2, respectively, in the practice block. At the end of the practice block, the last SSD time from each staircase was then carried over to be the initial SSD for the first block (in the case of multiple practice blocks, the SSD starting values returned to 200 and 300 ms at the start of each practice block); at the end of the first block, the last SSD time from each staircase was carried over to be the initial SSD for the second block. This one-up/one-down tracking procedure ensured that subjects successfully inhibited on approximately 50% of inhibition trials. Also as a result, difficulty level is individualized across subjects and both behavioral performance and numbers of successful stop trials are equated across subjects.<br />
<br />
All participants received training on the Stop-signal task in the form of (at least) one practice block and two experiment blocks. The first practice block contained 22 Go trials and 10 stop trials presented randomly. Subjects were required to score over 20% accuracy on Stop practice trials and have average Go RT less than 750 ms in order to continue. Each experiment block contained 128 trials per block, 96 of which were Go trials and 32 of which were Stop trials (16 from Ladder 1, and 16 from Ladder 2), each presented randomly. Participants completed 2 successive blocks for a total of 256 trials. <br />
<br />
All trials started with a 500 ms fixation cross in the center of the screen and included a 1000 ms fixed response interval. Subjects were allowed to respond at the start of stimulus presentation until the end of the 1000 ms fixed response interval. Each trial was separated by a fixed 100 ms delay.<br />
<br />
Task presentation was administered via ePrime software. On each trial, the screen was black with white characters. The letters were written in Courier New font type (ePrime 26 size).<br />
<br />
=== Task Structure Detail ===<br />
''' This is what we had worked on before, but could use updating. We'd like to capture a schema that can handle each of the tasks in the CNP, so please think general when editing -fws'''<br />
<br />
* Task Structure ''(please given an overview of the task procedures here [i.e., overall design, block, trial, and within-trial event structure and timing])''<br />
** Participants performed at least one practice block and two experimental blocks<br />
*** Each experiment block contained 128 trials per block<br />
**** 96 Go trials <br />
**** 32 Stop trials <br />
***** 16 Stop trials from Ladder 1<br />
***** 16 Stop trials from Ladder 2 <br />
**** Participants completed 2 blocks for a total of 256 trials<br />
** Timing:<br />
*** All trials started with a 500 ms fixation cross in the center of the screen <br />
*** Each trial had a 1000 ms fixed response interval<br />
**** The onset of Go stimuli was the beginning of each trial’s 1000 ms fixed response interval<br />
**** The onset of Stop stimuli from two SSD ladders on a subset of trials (Stop trials) was variable<br />
***** SSD values for each of the two staircases started at 200 and 300 ms for ladder 1 and 2, respectively, in the practice block<br />
***** The last SSD time from each staircase in the practice block was carried over to be the initial SSD for the first block<br />
***** The last SSD time from each staircase in the first block was carried over to be the initial SSD for the second block<br />
**** Mean SSD ranged from approximately 65-650 ms across first 1000 participants (from 256 total trials)<br />
*** Each trial was separated by a fixed 100 ms delay<br />
<br />
* Stimulus Characteristics<br />
**sensory modality ''(e.g., visual, auditory, somatosensory, gustatory, olfactory)'': visual and auditory<br />
**presentation modality ''(e.g., human examiner, paper, computer display, headphones, speaker)'': computer display, headphones<br />
** Stimuli Descriptions:<br />
*** Go trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval<br />
*** Stop trials consisted of an “X” or “O” stimulus in the center of the screen for the 1000 ms fixed response interval and a stop-signal (500Hz tone), which was presented a variable delay after the onset of the go stimulus and lasted for 250 ms<br />
*** Stimuli were in ePrime (size setting 26), presented in white text on black background<br />
<br />
* Response Characteristics<br />
**responses required - Left arrow key press, Right arrow key press, or inhibition of key press<br />
***effector modality ''(e.g., vocal, manual, pedal)'': Manual response and response inhibition<br />
***functional modality ''(e.g., words, drawing, writing, keypress, movement)'': keypress and response inhibition<br />
**response options ''(e.g., yes/no, go/no-go, forced choice, multiple choice [specify n of options], free response)''- Go/No-Go<br />
**response collection ''(e.g., examiner notes, keyboard, keypad, mouse, voice key, button press)''- Keyboard button press<br />
**timing- see above<br />
<br />
=== Task Schematic ===<br />
<br />
Schemtic of the Stop-signal task.<br />
<br />
[[File:SST_Figure_2.png]]<br />
<br />
<br />
Schematic of the horse-race model of stopping and estimation of Stop-signal reaction time (SSRT).<br />
<br />
[[File:SSRT.png]]<br />
<br />
=== Task Parameters Table ===<br />
<br />
[[File:TaskParamTable.png]]<br />
<br />
=== Stimuli ===<br />
Go stimuli consisted of the letters “X” or “O”, which were presented in 26 size white Courier New font type in the center of a black screen. <br />
<br />
A stop stimulus was a 500 Hz tone presented through headphones, which was presented a short delay after the go stimulus appeared and lasted for 250 ms.<br />
<br />
A fixation stimulus was a fixation cross, which was presented in 26 size white Courier New font type in the center of a black screen.<br />
<br />
=== Dependent Variables ===<br />
The primary dependent variable is the Stop-signal reaction time (SSRT), which provides an individualized measure of inhibitory control. The use of a one-up/one-down tracking procedure ensures that participants inhibit on approximately half of all stop trials, which does not require an assumption of 50% inhibition, and allows us to use the quantile method to estimate SSRT (following Band et al., 2003). <br />
<br />
Other dependent variables that may be of interest include the mean RT on Go trials and the standard deviation of RT on Go trials. <br />
<br />
In contrast to other measures of response inhibition, such as the Go/No-Go task, percent inhibition is not a primary DV for the Stop-signal task, as this task is designed to ensure that participants inhibit on approximately 50% of all Stop trials. <br />
<br />
Additional summary measures can be used to screen outliers (see below). <br />
<br />
Table of all available variables.<br />
<br />
[[File:SST_Variables_Table.png]]<br />
<br />
=== Rules for Exclusion – Raw and Derived Variables ===<br />
1. If the number of trials is more or less than what is expected for each block (N = 128), that subject should be flagged for exclusion. If the inconsistency cannot be resolved, they should be excluded. <br />
<br />
2. If any of the derived variables (those listed in variables Table above) are missing, for either one or both testing blocks, that subject should be flagged for exclusion. <br />
=== Cleaning Rules – Derived Variables ===<br />
If any of the derived variables (those listed in variables Table above) are missing, for either one or both testing blocks, that participants should be flagged for exclusion. <br />
<br />
There are several decisions to make when estimating SSRT from more than one block of Stop-signal task performance data, including whether to average across all available sessions or to use the last session run (based on the assumption that participants are closest to their 50% inhibition point at the end of a session); whether to use all trials of each session or the last half only (again based on the assumption that participants stabilize inhibitory performance near the end of a session); and whether to use data from all participants that completed the task regardless of performance, or to use either conservative or lenient criteria to exclude outliers, so as to avoid violating assumptions underlying the race-model of stopping. These questions have been systematically assessed in an independent dataset, which was randomly split into halves in order to evaluate reliability and repeatability of SSRT estimates derived following multiple approaches to data cleaning (Congdon et al., 2012). Measures of reliability, including intraclass correlation coeffcients (ICC) and within-subject variability, and the resulting sample size, were used as indicators to evaluate the different strategies to data cleaning. <br />
<br />
Our results suggest that an approach that uses the average of all available Stop-signal blocks, all trials of each block, and excludes outliers based on predetermined lenient criteria (defined below) yields reliable SSRT estimates and low within-subject variability, while not excluding too many participants from the total dataset. Specifically, this approach resulted in an ICC value of 0.79 and a within-subject variability estimate of 25.42 ms, while only excluding 7 (out of 184) participants. Critically, this approach also retains a broad distribution of SSRT values. <br />
<br />
Based on these analyses, [[the following cleaning rules are suggested]]: <br />
* Use all trials from each testing block<br />
* Average across both testing blocks for final summary scores<br />
* Exclude outliers that meet the following lenient criteria:<br />
** Percent inhibition on Stop trials less than 25% or greater than 75%<br />
** Percent correct responding on Go trials less than 60%<br />
** Percent incorrect Go trials greater than 10%<br />
** SSRT estimate that is negative or less than 50 ms<br />
<br />
=== Code/Algorithms ===<br />
<br />
Scoring of behavioral data proceeded as follows. <br />
<br />
The mean, median and standard deviation of reaction time on Go trials were calculated only for Go trials in which participants correctly responded. Stop successful trials included only Stop trials on which participants successfully inhibited a response, and Stop unsuccessful trials included only Stop trials on which participants responded. Average stop-signal delay (SSD) was calculated from SSD values across both ladders. SSRT was estimated using the quantile method, which does not require an assumption of 50% inhibition (Band et al., 2003). Briefly, to calculate SSRT following the quantile method, all correct RTs from Go trials were arranged in ascending order. The proportion of failed inhibition, which is the proportion of Stop trials in which the participants responded, was calculated across both ladders. The quantileRT was determined by finding the RT corresponding to the proportion of failed inhibition. The average stop-signal delay (across both ladders) was then subtracted from the quantileRT in order to calculate SSRT (Band et al., 2003). <br />
<br />
Scores are calculated from each block separately and then averaged to provide summary scores for the overall session. It is recommended that one use the final measures based on the overall session, as they provide more stable estimates of SSRT (Band et al., 2003; Congdon et al., 2012). <br />
<br />
------<br />
<br />
Make 2 Filters: Procedure[Block] and Running[Block] and Procedure[Trial]<br />
<br />
First filter to only include trials where Procedure[Block] = “StopProc” (these are the real trials)<br />
<br />
Analyze the remaining trials in 2 different sets: Those with Running[Block] = “Block1” and those with Running[Block] = “Block2”<br />
<br />
For each of those two sets (Block1 and Block2) do the following steps:<br />
<br />
{<br />
<br />
* '''Block1_Direction_Errors''' = Number of trials where (Procedure[Trial] = "StGTrial" AND Go.RT> 0 AND Go.ACC= 0) OR (Procedure[Trial] = "StGTrial" AND Go.RT = 0 AND Blank.Resp != CorrectAnswer)<br />
* '''Block1_Percent_Go_Response''' = [Number of trials where Procedure[Trial] = "StGTrial" AND (Go.ACC=1 OR Blank.ACC=1)] / Number of trials where Procedure[Trial] = "StGTrial" <br />
<br />
For the next calculation, you need the following values:<br />
<br />
* Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND ('''GoAcc = 1 AND Go.RT > 0''')<br />
* Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND ('''GoACC = 0 AND Go.RT = 0 AND Blank.ACC = 1''')<br />
<br />
* '''Block1_Mean_RT''' = Mean of all values that you just got from Go.RT and Blank.RT + 1000<br />
* '''Block1_Median_RT''' = Median of all values that you just got from Go.RT and Blank.RT + 1000<br />
* '''Block1_StDev_RT''' = Standard deviation of all values that you just got from Go.RT and Blank.RT + 1000<br />
<br />
For the next step, you need to get have these numbers:<br />
<br />
* GoDur - 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2S.RT=0 and Blanks.RT=0)<br />
* GoDur + 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT!=0 or Inhs.RT!=0 or Go2S.RT!=0 or Blanks.RT!=0)<br />
* GoDur2 - 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)<br />
* GoDur2 + 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT!=0 or Inhs2.RT!=0 or Go2s2.RT!=0 or Blanks.RT!=0)<br />
<br />
* '''Block1_Ladder1Mean''' = Mean of all GoDur values from the previous statements (there should be 16 total)<br />
* '''Block1_Ladder2Mean''' = Mean of all GoDur2 values from the previous statements (there should be 16 total)<br />
* '''Block1_SSD50''' = Mean of ALL the values you just got from the previous statements (there should be 32 total values that you're taking the mean of)<br />
<br />
<br />
* '''Block1_PctInhib_Ladder1''' = [Number of Trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2s.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial")<br />
* '''Block1_PctInhib_Ladder2''' = [Number of Trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)] / (Number of Trials where Procedure[Trial] = "StITrial2)<br />
* '''Block1_Percent_Inhib''' = Mean of Block1_PctInhib_Ladder1 and Block1_PctInhib_Ladder2<br />
<br />
* '''Block1_Quantile_Value''' = 1 - Block1_Percent_Inhib<br />
<br />
* '''Block1_SSRT''' = Block1_Median_RT - Block1_SSD50<br />
<br />
* CorrectGoRT on StopProc trials: As a reminder, these are Go.RT for all trials where (Procedure[Trial] = "StGTrial") AND (GoAcc = 1 AND Go.RT > 0), and Blank.RT + 1000 for all trials where (Procedure[Trial] = "StGTrial") AND (GoACC = 0 AND Go.RT = 0 AND Blank.ACC = 1). Use these CorrectGoRT on StopProc trials to calculate Block1_Quant_RT:<br />
<br />
* '''Block1_Quant_RT''' = Quantile calculation of all the values from Go.RT and Blank.RT + 1000 (the values used in the calculations where you calculated Block1_Mean_RT, etc), taken using Quantile_Value. So if Quantile_Value = .85, you'd take the .85-quantile of the distribution of values from Go.RT and (Blank.RT + 1000)<br />
<br />
* '''Block1_SSRT_Quant''' = Block1_Quant_RT - Block1_SSD50<br />
<br />
}<br />
<br />
Then, do all those same calculations that were in the braces, but using the data points that were from Running[Block] = “Block2” (and save the variables as Block2_whatever, rather than Block1)<br />
<br />
Then average the above values together to get a measure of overall task performance: <br />
<br />
All the values that we need outputted are in bold. See also Variable List. <br />
<br />
-------<br />
<br />
The above text instructions can be adapted for any program. These instructions are currently implemented in Stone’s scoring scripts for the scoring of LA2K variables. These steps have been implemented in separate Matlab scripts by Eliza and have been used to verify Stone’s scoring scripts. These steps have also been used to manually score the data and verify Stone’s scoring scripts multiple times (finalized April 2010). <br />
<br />
''Note that one thing that may differ in the scoring script between programs is quantile calculation. For example, the quantile function in Matlab differs slightly from the percentile function in Excel. Stone’s scoring method may differ by a tiny fraction from the Matlab quantile function; however, this difference is tiny, and as long as the same scoring script is applied to all subjects (as it is in LA2K), then it does not matter. All agreed April 2010 on this issue.<br />
''<br />
<br />
''Note also to users interested in trial-by-trial data. Do not use the GoDur and GoDur2 values as SSD values. These are placeholders only and do not reflect actual SSD values, which tracked subject’s performance. Instead, you must apply the following code to the GoDur and GoDur2 values in order to get accurate SSD_1 and SSD_2 values, respectively: <br />
* GoDur - 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT=0 and Inhs.RT=0 and Go2S.RT=0 and Blanks.RT=0)<br />
* GoDur + 50 for all trials where Procedure[Trial] = "StITrial" AND (Go1s.RT!=0 or Inhs.RT!=0 or Go2S.RT!=0 or Blanks.RT!=0)<br />
* GoDur2 - 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT=0 and Inhs2.RT=0 and Go2s2.RT=0 and Blanks.RT=0)<br />
* GoDur2 + 50 for all trials where Procedure[Trial] = "StITrial2" AND (Go1s2.RT!=0 or Inhs2.RT!=0 or Go2s2.RT!=0 or Blanks.RT!=0)<br />
''<br />
History of Checking Scoring:<br />
* October 2009: Eliza confirmed Stone’s scripts by manually scoring data (completed 10/19/2009)<br />
<br />
* January 2010: Another group (Nicole McLaughlin at Butler Hospital) that was sent the scoring scripts identified a scoring error. <br />
** Eliza and Stone worked together to resolve the problem. Stone fixed that error in the script on 1/29/2010. <br />
** Eliza and Stone clarified an issue about the scoring of Go trial (direction) errors and matched scores between UCLA and Butler (March 2010). <br />
** Final questions about differences in quantile calculation were resolved in April. Eliza and Stone calculated all scores on the same data and met to confirm scoring scripts. Everything agreed and finalized 4/21/10. <br />
<br />
* Eliza confirmed SSD starting values, and updated trial-by-trial data, on 8/13/11.<br />
<br />
=== Data Distributions ===<br />
<br />
These are based on Congdon Query 80 (7/27/11)-Derived Data; Query 82 (7/27/11)-Trial-by-Trial Data; and updated with Congdon Query (1/17/12).<br />
<br />
[[File:1_Results.png]]<br />
<br />
[[File:2_Results.png]]<br />
<br />
[[File:3_Results.png]]<br />
<br />
[[File:4_Results.png]]<br />
<br />
<br />
Updated 8/2/12 by EC: <br/><br />
The following subjects should be excluded for the following reasons: <br/><br />
* Missing key derived variables because of poor performance: 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198. <br />
* Percent Inhibition outside the allowable range: 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076<br />
* Too many Go errors: 10176<br />
* In addition to those that just have missing SST data.<br />
<br />
=== References ===<br />
Band, G. P., van der Molen, M. W., and Logan, G. D. (2003). Horse-race model simulations of the stop-signal procedure. ''Acta Psychol, 112,'' 105-142.<br />
<br />
Boucher, L., Palmeri, T. J., Logan, G. D., and Schall, J. D. (2007). Inhibitory control in mind and brain: An interactive race model of countermanding saccades. ''Psychol Rev, 114 (2),'' 376-97. <br />
<br />
Congdon, E., Mumford, J., Cohen, J., Galvan, A., Canli, T., & Poldrack, R. A. (2012). Measurement and reliability of response inhibition. ''Frontiers in Psychology, 3 (37),'' doi: 10.3389/fpsyg.2012.00037.<br />
<br />
Logan, G. D., and Cowan, W. B. (1984). On the ability to inhibit through and action: a theory of an act of control. ''Psychol Rev, 91,'' 295-327.<br />
<br />
Logan, G. D. (1994). On the ability to inhibit thought and action: A users’ guide to the stop signal paradigm. In: Dagenbach, D., Carr, T. H. (Eds.), Inhibitory Processes in Attention, Memory and Language. Academic Press, San Diego, pp. 189-239.<br />
<br />
=== Methods Synopsis ===<br />
<br />
Participants completed a tracking Stop-signal task, which is based on a horse-race model of response inhibition (Logan & Cowan, 1984; Logan, 1994). Participants were presented with a series of Go stimuli (“X” or “O”) to which they were instructed to respond quickly (with a left or right button press, respectively) and, on a subset of trials (25%), the Go stimulus was followed, after a variable delay, by a stop-signal (a 500 Hz tone presented through headphones). Participants were instructed to inhibit responses on trials in which the stop-signal appeared, and that correctly responding and inhibiting were equally important. After completing at least one practice block of 32 trials (22 of which were Go trials), participants completed two blocks of 128 trials each, for a total of 256 trials.<br />
<br />
On Stop trials, the delay of the onset of the stop-signal, or stop-signal delay (SSD), was varied, such that it was increased after the participant successfully inhibited in response to a stop-signal (making the next stop trial more difficult), and decreased after the participant failed to inhibit in response to a stop-signal (making the next stop trial less difficult). Each SSD increase or decrease was in 50 ms intervals. The SSD values were drawn from two interleaved staircases (or ladders) per block, resulting in 16 trials from each staircase for a total of 32 Stop trials per block. SSD values for ladders 1 and 2 began with SSD values of 200 and 300 ms, respectively, in the practice block. At the end of the practice and first blocks, the last SSD times from each staircase were then carried over to be the initial SSD values for the first and second blocks, respectively. <br />
<br />
All trials started with a 500 ms fixation cross in the center of the screen and included a 1000 ms fixed response interval. Subjects were allowed to respond at the start of stimulus presentation until the end of the 1000 ms fixed response interval. Each trial was separated by a fixed 100 ms delay. Task presentation was administered via ePrime software.<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=PAM&diff=8494PAM2013-12-21T00:03:11Z<p>Elizac: /* PAM Models */</p>
<hr />
<div>Back to [[LA5C]]<br />
<br />
=Background=<br />
This task was run as a part of the Consortium for Neuropsychiatric Phenomics ([[CNP]]) project. It was designed in collaboration with Russ Poldrack, Theo van Erp, and Becca Schwarzlose. It is an associative memory task in which subjects view pairs of objects and must remember not only whether they have seen them before but how they were originally paired.<br />
<br />
=Task Design=<br />
==PAM Encoding (PAMenc)==<br />
For all encoding trials, one figure is in black and white, and one is in color (orange). The subject must indicate by button press which side the colored object is on (this is the same as the RK encoding paradigm, but different from the NAPLS PAM encoding). Subjects are instructed to remember the objects and the relationship between the objects. The ITI is jittered.<br />
<br />
The encoding task consists of 64 trials:<br/><br />
*24 control trials- pairs of scrambled stimuli<br/><br />
*40 memory trials- pairs of line drawings of objects<br/><br />
<br />
*Control trials last 2 seconds<br/><br />
*Encoding trials last 4 seconds (1 with just words, and then 3 for words + pictures<br/><br />
*All time that is not accounted for in between trials is “null”<br/><br />
<br />
PAMenc is 242 TRs long, with a TR of 2000ms.<br />
<br />
==PAM Retrieval (PAMret)==<br />
The retrieval task requires the subjects to rate their confidence in their memory of the pairing. There are 4 possible response options ranging from "Sure correct" to "Sure incorrect". These can be analyzed later as a spectrum, or binarized into yes/no type responses.<br />
<br />
The retrieval task consists of 104 trials<br/><br />
*24 control trials- on one side of the screen is one of the 4 retrieval confidence response options "Sure correct", "maybe correct", "maybe incorrect", or "sure incorrect". On the other side of the screen is "xxxx". Subjects are asked to press the button (1-4) that corresponds to the response option displayed<br/><br />
*40 correct trials- items are shown paired as they were at encoding<br/><br />
*40 incorrect trials- items are shown paired differently than they were at encoding (some objects are the same, j ust paired incorrectly)<br/><br />
<br />
PAMret is 268 TRs long, with a TR of 2000ms.<br />
<br />
==Design Documentation==<br />
These files were created by Theo, to show the trial-by-trial information including onsets and delays. The layout is meant to be compatible with E-Prime. They open in Excel.<br/><br />
[[:Media:PAMenc_trialinfo.xlsx]]<br/><br />
[[:Media:PAMret_trialinfo.xlsx]]<br/><br />
<br />
These files were created by Theo to show the stimuli pairing and presentation order. They open in excel. <br/><br />
[[:Media:PAMenc_stims.xlsx]]<br/><br />
[[:Media:PAMret_stims.xlsx]]<br/><br />
<br />
These files were created by Eric Miller, the CNP RA and are designed so that the trial information is easier to interpret in relation to the matlab output.<br/><br />
[[:Media:Pamenc_actualtrialinfo.xlsx]]<br/><br />
[[:Media:Pamrec_actualtrialinfo.xlsx ]]<br/><br />
<br />
=Analysis=<br />
The PAM is divided into two sections, PAMENC and PAMRET for the encoding and retrieval phases. In the subjects directory, these are separate, but in the scripts directory they are not. The tasks are linked together for scoring by two files, rectrialcodes_during enc and enctrialcodes_duringrec to keep track of how the trials in each section correspond to each other.<br />
<br />
'''IMPORTANT''': Partway into the study, the PAM underwent a substantial change. We are only concerned with data after this change. The first subject on the new version is 10506. All files that related to the new version are labeled with _fixed.<br />
<br />
==Scoring Behavioral Data==<br />
/space/raid2/data/poldrack/CNP/scripts/behav_analyze/PAM<br />
<br />
All scripts pull up the file ‘sublist_pamenc’ and “sublist_pamrec”to determine which subjects to run. Before you run a new batch, edit that file using emacs (emacs sublist_pamenc). IDs are in the format of CNP_12345A. It is just a transient file, so you can delete what is in there. If you’d like to save the old version, just save it as sublist with the date appended. The script will only recognize the plain ‘sublist; files.<br />
<br />
'''PAMENC'''<br/><br />
*in matlab, run score_pamenc_behavior.m<br/><br />
*this should create something called summaryscore_PAMENC_fixed.txt in everybodies behav/PAMENC folders; You can check who has these files (and therefore who needs to be run) by typing <br/><br />
ls /space/raid2/data/poldrack/CNP/CONTROLS/*A/behav/PAMENC/*fixed*<br />
<br />
to summarize the data, edit make_big_pamenc_fixed_score_log.sh with the appropriate group and date. Then run, it will create a summaryscore_output and summaryscore_subjlist file.<br />
<br />
'''PAMRET'''<br/><br />
*in matlab run score_pamrec_behavior.m<br/><br />
*this should create something called summaryscore_PAMRET_fixed.txt in everybodies behav/PAMENC folders; You can check who has these files (and therefore who needs to be run) by typing <br/><br />
ls /space/raid2/data/poldrack/CNP/CONTROLS/*A/behav/PAMRET/*fixed*<br />
<br />
to summarize the data, edit make_big_pamret_fixed_score_log.sh with the appropriate group and date. Then run, it will create a summaryscore_output and summaryscore_subjlist file.<br />
<br />
You can now copy these into excel, although you might need to use the ‘text to columns’ tool to get each number to go into its own cell.<br />
<br />
==Creating Onset Files (EVs)==<br />
/space/raid2/data/poldrack/CNP/scripts/behav_analyze/PAM<br/><br />
<br />
These scripts also uses the sublist files- so, you can easily run the behavioral scoring and these scripts on the same list of new people. Update sublist_pamenc and sublist_pamrec as described above.<br/><br />
<br />
'''PAMENC'''<br/><br />
in matlab, run make_pamenc_model1_onsets_function.m<br/><br />
<br />
running this will create a series of files in each persons own behav/PAMENC. After both scripts have been run, the folder should look like this:<br/><br />
<br />
PAMenc_fixed_10638.mat <br/> <br />
pamenc_onsets_model1_hiconf_miss.txt <br/> <br />
pamenc_onsets_model1_loconf_miss.txt<br/><br />
pamenc_onsets_model1_control.txt <br/> <br />
pamenc_onsets_model1_junk.txt <br/> <br />
summaryscore_PAMENC_fixed.txt<br/><br />
pamenc_onsets_model1_hiconf_hit.txt <br/><br />
pamenc_onsets_model1_loconf_hit.txt<br/><br />
<br />
The onset files will have contents that look something like this:<br/><br />
22.0397 4 1<br/><br />
92.5125 4 1<br/><br />
135.0052 4 1<br/><br />
155.5230 4 1<br/><br />
220.5196 4 1<br/><br />
226.0131 4 1<br/><br />
226.0132 4 1<br/><br />
240.0118 4 1<br/><br />
261.5227 4 1<br/><br />
292.0186 4 1<br/><br />
324.0038 4 1<br/><br />
347.0201 4 1<br/><br />
347.0205 4 1<br/><br />
347.0205 4 1<br/><br />
376.5071 4 1<br/><br />
412.0166 4 1<br/><br />
412.0167 4 1<br/><br />
443.5219 4 1<br/><br />
443.5219 4 1<br/><br />
448.0226 4 1<br/><br />
448.0227 4 1<br/><br />
448.0227 4 1<br/><br />
473.5214 4 1<br/><br />
473.5215 4 1<br/><br />
<br />
<br />
'''PAMRET'''<br/><br />
in matlab, run make_pamrec_model1_onsets_function.m<br/><br />
<br />
running this will create a series of files in each persons own behav/PAMRET. After both scripts have been run, the folder should look like this:<br/><br />
<br />
pamrec_onsets_model1_all_incorr.txt<br/><br />
pamrec_onsets_model1_controltrial.txt<br/><br />
pamrec_onsets_model1_hiconfno_corr.txt<br/><br />
pamrec_onsets_model1_hiconfno_incorr.txt<br/><br />
pamrec_onsets_model1_hiconfyes_corr.txt<br/><br />
pamrec_onsets_model1_hiconfyes_incorr.txt<br/><br />
pamrec_onsets_model1_lowconfno_corr.txt<br/><br />
pamrec_onsets_model1_lowconfno_incorr.txt<br/><br />
pamrec_onsets_model1_lowconfyes_corr.txt<br/><br />
pamrec_onsets_model1_lowconfyes_incorr.txt<br/><br />
pamrec_onsets_model3_controltrial.txt<br/><br />
pamrec_onsets_model3_falseneg.txt<br/><br />
pamrec_onsets_model3_falsepos.txt<br/><br />
pamrec_onsets_model3_trueneg.txt<br/><br />
pamrec_onsets_model3_truepos.txt<br/><br />
PAMret_fixed_11062.mat<br/><br />
summaryscore_PAMRET_fixed.txt<br/><br />
trialcount_PAMRET_model3.txt<br/><br />
trialcount_PAMRET.txt<br/><br />
<br />
==PAM Models==<br />
'''PAMENC'''<br />
*'''Model1''' - PamEnc only has one model, which includes each of these conditions:<br />
:control -- scrambled trials<br/><br />
:hiconf_hit -- responded to correctly at retrieval with high confidence<br/><br />
:hiconf_miss -- responded to incorrectly at retrieval with high confidence<br/><br />
:loconf_hit -- responded to correctly at retrieval with low confidence<br/><br />
:loconf_miss -- responded to incorrectly at retrieval with low confidence<br/><br />
:junk -- missed trials, motion, etc<br/><br />
<br />
'''* UPDATE 12/20/13: Use PAMENC model_1a because it has errors fixed.<br />
<br />
'''<br />
'''PAMRET'''<br />
*'''Model1'''- Model 1 is the most basic version, and includes all of the possible conditions. The problem with model 1 is that often people have missing conditions (most frequently those that are something like "hiconf yes- incorr"). Because of this, doing group analyses, in which there cannot be missing conditions, is challenging.<br />
<br />
'''Retrieval Model1 onsets are:'''<br/><br />
:hiconfno_corr -- items which got a "sure incorrect" response that were indeed incorrectly paired<br/><br />
:hiconfno_incorr -- items which got a "sure incorrect" response that were actually correctly paired<br/><br />
:hiconfyes_corr -- items which got a "sure correct" response that were indeed correctly paired <br/><br />
:hiconfyes_incorr -- items which got a "sure correct" response that were actually incorrectly paired<br/><br />
:lowconfno_corr -- items which got a "maybe incorrect response that were indeed incorrectly paired<br/><br />
:lowconfno_incorr -- items which got a "maybe incorrect" response that were actually correctly paired<br/><br />
:lowconfyes_corr -- items which got a "maybe correct" response that were indeed correctly paired<br/><br />
:lowconfyes_incorr -- items which got a "maybe correct" response that were actually incorrectly paired<br/><br />
:control trials<br />
<br />
*'''Model2'''- Model 2 is similar to Model1, except that the incorrect conditions, which were frequently empty, have been combined into a single "incorrect" condition.<br />
<br />
'''Retrieval Model2 onsets are:'''<br/><br />
:hiconfno_corr -- items which got a "sure incorrect" response that were indeed incorrectly paired<br/><br />
:hiconfno_incorr -- items which got a "sure incorrect" response that were actually correctly paired<br/><br />
:hiconfyes_corr -- items which got a "sure correct" response that were indeed correctly paired <br/><br />
:hiconfyes_incorr -- items which got a "sure correct" response that were actually incorrectly paired<br/><br />
:lowconfno_corr -- items which got a "maybe incorrect response that were indeed incorrectly paired<br/><br />
:lowconfno_incorr -- items which got a "maybe incorrect" response that were actually correctly paired<br/><br />
:lowconfyes_corr -- items which got a "maybe correct" response that were indeed correctly paired<br/><br />
:lowconfyes_incorr -- items which got a "maybe correct" response that were actually incorrectly paired<br/><br />
:control trials<br />
<br />
*'''Model3'''- Model 3 was designed to have the least number of missing conditions. It models only the signal detection type conditions for the task.<br />
<br />
'''Retrieval Model3 onsets are:'''<br/><br />
:falseneg<br />
:falsepos<br />
:trueneg<br />
:truepos<br />
:control trials<br />
<br />
<br />
For each model, since missing conditions is such an issue, you can look at the trialcount.txt files (trialcount_PAMRET_model3.txt is for model 3, and trialcount_PAMRET.txt is for models 1 and 2). You may want to request this, along with the summaryscore_PAMRET_fixed.txt file along with your imaging files.<br />
<br />
==Running First Levels==<br />
/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts<br/><br />
<br />
The primary scripts for running first levels are:<br/><br />
/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts/PAMENC/PAMENC_firstlevel_model1.sh<br/><br />
/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts/PAMRET/PAMRET_firstlevel_model1.sh<br/><br />
<br />
These do the first phase of PAMENC and PAMRET fMRI processing. They check for the relevant files, create an individualized .fsf file for each subject, run pre- and post stats.<br/><br />
<br />
Each script takes 4 arguments:<br/><br />
1 group vs subject analysis, <br/><br />
2. population (CONTROL, SCHZ, etc) <br/><br />
3. which subject to run <br/><br />
4. whether to run FSL or just create the fsf file (run or norun)<br/><br />
<br />
There are a few ways you can run it:<br/><br />
a. to run on one person (here, CNP_10159A) and run FSL, go to the appropriate directory,<br/><br />
./PAMENC_firstlevel_model1.sh subject CONTROLS 10159 run<br/><br />
<br />
b. to run on an entire group (all controls, all patients, etc)<br/><br />
./PAMRET_firstlevel_model1.sh group CONTROLS all run<br/><br />
<br />
c. to run a specific group of people, you can use a second script that calls this one, run_multiple_scap.sh. for this script, you have to edit it first using emacs, and basically fill in the people you want to run in the for-loop at the top, for instance<br/><br />
for id in 10523 10501 10159; do<br/><br />
<br />
you also need to edit the other relevant options, such as population and whether to run all the way through. It’ll automatically run in single-subject mode, and just loop through these people.<br/><br />
<br />
This can also be submitted to the grid, after it is edited, by typing<br/><br />
sge qsub run_multiple_pamenc.sh<br/><br />
<br />
==Checking Data==<br />
Data for this paradigm are logged on the HTAC data base. To get access contact Stone Shih or Fred Sabb.<br />
<br />
=Publications=<br />
<br />
----<br />
Link back to [[LA5C]] page.</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=PAM&diff=8493PAM2013-12-21T00:02:29Z<p>Elizac: /* PAM Models */</p>
<hr />
<div>Back to [[LA5C]]<br />
<br />
=Background=<br />
This task was run as a part of the Consortium for Neuropsychiatric Phenomics ([[CNP]]) project. It was designed in collaboration with Russ Poldrack, Theo van Erp, and Becca Schwarzlose. It is an associative memory task in which subjects view pairs of objects and must remember not only whether they have seen them before but how they were originally paired.<br />
<br />
=Task Design=<br />
==PAM Encoding (PAMenc)==<br />
For all encoding trials, one figure is in black and white, and one is in color (orange). The subject must indicate by button press which side the colored object is on (this is the same as the RK encoding paradigm, but different from the NAPLS PAM encoding). Subjects are instructed to remember the objects and the relationship between the objects. The ITI is jittered.<br />
<br />
The encoding task consists of 64 trials:<br/><br />
*24 control trials- pairs of scrambled stimuli<br/><br />
*40 memory trials- pairs of line drawings of objects<br/><br />
<br />
*Control trials last 2 seconds<br/><br />
*Encoding trials last 4 seconds (1 with just words, and then 3 for words + pictures<br/><br />
*All time that is not accounted for in between trials is “null”<br/><br />
<br />
PAMenc is 242 TRs long, with a TR of 2000ms.<br />
<br />
==PAM Retrieval (PAMret)==<br />
The retrieval task requires the subjects to rate their confidence in their memory of the pairing. There are 4 possible response options ranging from "Sure correct" to "Sure incorrect". These can be analyzed later as a spectrum, or binarized into yes/no type responses.<br />
<br />
The retrieval task consists of 104 trials<br/><br />
*24 control trials- on one side of the screen is one of the 4 retrieval confidence response options "Sure correct", "maybe correct", "maybe incorrect", or "sure incorrect". On the other side of the screen is "xxxx". Subjects are asked to press the button (1-4) that corresponds to the response option displayed<br/><br />
*40 correct trials- items are shown paired as they were at encoding<br/><br />
*40 incorrect trials- items are shown paired differently than they were at encoding (some objects are the same, j ust paired incorrectly)<br/><br />
<br />
PAMret is 268 TRs long, with a TR of 2000ms.<br />
<br />
==Design Documentation==<br />
These files were created by Theo, to show the trial-by-trial information including onsets and delays. The layout is meant to be compatible with E-Prime. They open in Excel.<br/><br />
[[:Media:PAMenc_trialinfo.xlsx]]<br/><br />
[[:Media:PAMret_trialinfo.xlsx]]<br/><br />
<br />
These files were created by Theo to show the stimuli pairing and presentation order. They open in excel. <br/><br />
[[:Media:PAMenc_stims.xlsx]]<br/><br />
[[:Media:PAMret_stims.xlsx]]<br/><br />
<br />
These files were created by Eric Miller, the CNP RA and are designed so that the trial information is easier to interpret in relation to the matlab output.<br/><br />
[[:Media:Pamenc_actualtrialinfo.xlsx]]<br/><br />
[[:Media:Pamrec_actualtrialinfo.xlsx ]]<br/><br />
<br />
=Analysis=<br />
The PAM is divided into two sections, PAMENC and PAMRET for the encoding and retrieval phases. In the subjects directory, these are separate, but in the scripts directory they are not. The tasks are linked together for scoring by two files, rectrialcodes_during enc and enctrialcodes_duringrec to keep track of how the trials in each section correspond to each other.<br />
<br />
'''IMPORTANT''': Partway into the study, the PAM underwent a substantial change. We are only concerned with data after this change. The first subject on the new version is 10506. All files that related to the new version are labeled with _fixed.<br />
<br />
==Scoring Behavioral Data==<br />
/space/raid2/data/poldrack/CNP/scripts/behav_analyze/PAM<br />
<br />
All scripts pull up the file ‘sublist_pamenc’ and “sublist_pamrec”to determine which subjects to run. Before you run a new batch, edit that file using emacs (emacs sublist_pamenc). IDs are in the format of CNP_12345A. It is just a transient file, so you can delete what is in there. If you’d like to save the old version, just save it as sublist with the date appended. The script will only recognize the plain ‘sublist; files.<br />
<br />
'''PAMENC'''<br/><br />
*in matlab, run score_pamenc_behavior.m<br/><br />
*this should create something called summaryscore_PAMENC_fixed.txt in everybodies behav/PAMENC folders; You can check who has these files (and therefore who needs to be run) by typing <br/><br />
ls /space/raid2/data/poldrack/CNP/CONTROLS/*A/behav/PAMENC/*fixed*<br />
<br />
to summarize the data, edit make_big_pamenc_fixed_score_log.sh with the appropriate group and date. Then run, it will create a summaryscore_output and summaryscore_subjlist file.<br />
<br />
'''PAMRET'''<br/><br />
*in matlab run score_pamrec_behavior.m<br/><br />
*this should create something called summaryscore_PAMRET_fixed.txt in everybodies behav/PAMENC folders; You can check who has these files (and therefore who needs to be run) by typing <br/><br />
ls /space/raid2/data/poldrack/CNP/CONTROLS/*A/behav/PAMRET/*fixed*<br />
<br />
to summarize the data, edit make_big_pamret_fixed_score_log.sh with the appropriate group and date. Then run, it will create a summaryscore_output and summaryscore_subjlist file.<br />
<br />
You can now copy these into excel, although you might need to use the ‘text to columns’ tool to get each number to go into its own cell.<br />
<br />
==Creating Onset Files (EVs)==<br />
/space/raid2/data/poldrack/CNP/scripts/behav_analyze/PAM<br/><br />
<br />
These scripts also uses the sublist files- so, you can easily run the behavioral scoring and these scripts on the same list of new people. Update sublist_pamenc and sublist_pamrec as described above.<br/><br />
<br />
'''PAMENC'''<br/><br />
in matlab, run make_pamenc_model1_onsets_function.m<br/><br />
<br />
running this will create a series of files in each persons own behav/PAMENC. After both scripts have been run, the folder should look like this:<br/><br />
<br />
PAMenc_fixed_10638.mat <br/> <br />
pamenc_onsets_model1_hiconf_miss.txt <br/> <br />
pamenc_onsets_model1_loconf_miss.txt<br/><br />
pamenc_onsets_model1_control.txt <br/> <br />
pamenc_onsets_model1_junk.txt <br/> <br />
summaryscore_PAMENC_fixed.txt<br/><br />
pamenc_onsets_model1_hiconf_hit.txt <br/><br />
pamenc_onsets_model1_loconf_hit.txt<br/><br />
<br />
The onset files will have contents that look something like this:<br/><br />
22.0397 4 1<br/><br />
92.5125 4 1<br/><br />
135.0052 4 1<br/><br />
155.5230 4 1<br/><br />
220.5196 4 1<br/><br />
226.0131 4 1<br/><br />
226.0132 4 1<br/><br />
240.0118 4 1<br/><br />
261.5227 4 1<br/><br />
292.0186 4 1<br/><br />
324.0038 4 1<br/><br />
347.0201 4 1<br/><br />
347.0205 4 1<br/><br />
347.0205 4 1<br/><br />
376.5071 4 1<br/><br />
412.0166 4 1<br/><br />
412.0167 4 1<br/><br />
443.5219 4 1<br/><br />
443.5219 4 1<br/><br />
448.0226 4 1<br/><br />
448.0227 4 1<br/><br />
448.0227 4 1<br/><br />
473.5214 4 1<br/><br />
473.5215 4 1<br/><br />
<br />
<br />
'''PAMRET'''<br/><br />
in matlab, run make_pamrec_model1_onsets_function.m<br/><br />
<br />
running this will create a series of files in each persons own behav/PAMRET. After both scripts have been run, the folder should look like this:<br/><br />
<br />
pamrec_onsets_model1_all_incorr.txt<br/><br />
pamrec_onsets_model1_controltrial.txt<br/><br />
pamrec_onsets_model1_hiconfno_corr.txt<br/><br />
pamrec_onsets_model1_hiconfno_incorr.txt<br/><br />
pamrec_onsets_model1_hiconfyes_corr.txt<br/><br />
pamrec_onsets_model1_hiconfyes_incorr.txt<br/><br />
pamrec_onsets_model1_lowconfno_corr.txt<br/><br />
pamrec_onsets_model1_lowconfno_incorr.txt<br/><br />
pamrec_onsets_model1_lowconfyes_corr.txt<br/><br />
pamrec_onsets_model1_lowconfyes_incorr.txt<br/><br />
pamrec_onsets_model3_controltrial.txt<br/><br />
pamrec_onsets_model3_falseneg.txt<br/><br />
pamrec_onsets_model3_falsepos.txt<br/><br />
pamrec_onsets_model3_trueneg.txt<br/><br />
pamrec_onsets_model3_truepos.txt<br/><br />
PAMret_fixed_11062.mat<br/><br />
summaryscore_PAMRET_fixed.txt<br/><br />
trialcount_PAMRET_model3.txt<br/><br />
trialcount_PAMRET.txt<br/><br />
<br />
==PAM Models==<br />
'''PAMENC'''<br />
*'''Model1''' - PamEnc only has one model, which includes each of these conditions:<br />
:control -- scrambled trials<br/><br />
:hiconf_hit -- responded to correctly at retrieval with high confidence<br/><br />
:hiconf_miss -- responded to incorrectly at retrieval with high confidence<br/><br />
:loconf_hit -- responded to correctly at retrieval with low confidence<br/><br />
:loconf_miss -- responded to incorrectly at retrieval with low confidence<br/><br />
:junk -- missed trials, motion, etc<br/><br />
<br />
'''* UPDATE 12/20/13: Use PAMENC model_1a because it has errors fixed.<br />
'''<br />
'''PAMRET'''<br />
*'''Model1'''- Model 1 is the most basic version, and includes all of the possible conditions. The problem with model 1 is that often people have missing conditions (most frequently those that are something like "hiconf yes- incorr"). Because of this, doing group analyses, in which there cannot be missing conditions, is challenging.<br />
<br />
'''Retrieval Model1 onsets are:'''<br/><br />
:hiconfno_corr -- items which got a "sure incorrect" response that were indeed incorrectly paired<br/><br />
:hiconfno_incorr -- items which got a "sure incorrect" response that were actually correctly paired<br/><br />
:hiconfyes_corr -- items which got a "sure correct" response that were indeed correctly paired <br/><br />
:hiconfyes_incorr -- items which got a "sure correct" response that were actually incorrectly paired<br/><br />
:lowconfno_corr -- items which got a "maybe incorrect response that were indeed incorrectly paired<br/><br />
:lowconfno_incorr -- items which got a "maybe incorrect" response that were actually correctly paired<br/><br />
:lowconfyes_corr -- items which got a "maybe correct" response that were indeed correctly paired<br/><br />
:lowconfyes_incorr -- items which got a "maybe correct" response that were actually incorrectly paired<br/><br />
:control trials<br />
<br />
*'''Model2'''- Model 2 is similar to Model1, except that the incorrect conditions, which were frequently empty, have been combined into a single "incorrect" condition.<br />
<br />
'''Retrieval Model2 onsets are:'''<br/><br />
:hiconfno_corr -- items which got a "sure incorrect" response that were indeed incorrectly paired<br/><br />
:hiconfno_incorr -- items which got a "sure incorrect" response that were actually correctly paired<br/><br />
:hiconfyes_corr -- items which got a "sure correct" response that were indeed correctly paired <br/><br />
:hiconfyes_incorr -- items which got a "sure correct" response that were actually incorrectly paired<br/><br />
:lowconfno_corr -- items which got a "maybe incorrect response that were indeed incorrectly paired<br/><br />
:lowconfno_incorr -- items which got a "maybe incorrect" response that were actually correctly paired<br/><br />
:lowconfyes_corr -- items which got a "maybe correct" response that were indeed correctly paired<br/><br />
:lowconfyes_incorr -- items which got a "maybe correct" response that were actually incorrectly paired<br/><br />
:control trials<br />
<br />
*'''Model3'''- Model 3 was designed to have the least number of missing conditions. It models only the signal detection type conditions for the task.<br />
<br />
'''Retrieval Model3 onsets are:'''<br/><br />
:falseneg<br />
:falsepos<br />
:trueneg<br />
:truepos<br />
:control trials<br />
<br />
<br />
For each model, since missing conditions is such an issue, you can look at the trialcount.txt files (trialcount_PAMRET_model3.txt is for model 3, and trialcount_PAMRET.txt is for models 1 and 2). You may want to request this, along with the summaryscore_PAMRET_fixed.txt file along with your imaging files.<br />
<br />
==Running First Levels==<br />
/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts<br/><br />
<br />
The primary scripts for running first levels are:<br/><br />
/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts/PAMENC/PAMENC_firstlevel_model1.sh<br/><br />
/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts/PAMRET/PAMRET_firstlevel_model1.sh<br/><br />
<br />
These do the first phase of PAMENC and PAMRET fMRI processing. They check for the relevant files, create an individualized .fsf file for each subject, run pre- and post stats.<br/><br />
<br />
Each script takes 4 arguments:<br/><br />
1 group vs subject analysis, <br/><br />
2. population (CONTROL, SCHZ, etc) <br/><br />
3. which subject to run <br/><br />
4. whether to run FSL or just create the fsf file (run or norun)<br/><br />
<br />
There are a few ways you can run it:<br/><br />
a. to run on one person (here, CNP_10159A) and run FSL, go to the appropriate directory,<br/><br />
./PAMENC_firstlevel_model1.sh subject CONTROLS 10159 run<br/><br />
<br />
b. to run on an entire group (all controls, all patients, etc)<br/><br />
./PAMRET_firstlevel_model1.sh group CONTROLS all run<br/><br />
<br />
c. to run a specific group of people, you can use a second script that calls this one, run_multiple_scap.sh. for this script, you have to edit it first using emacs, and basically fill in the people you want to run in the for-loop at the top, for instance<br/><br />
for id in 10523 10501 10159; do<br/><br />
<br />
you also need to edit the other relevant options, such as population and whether to run all the way through. It’ll automatically run in single-subject mode, and just loop through these people.<br/><br />
<br />
This can also be submitted to the grid, after it is edited, by typing<br/><br />
sge qsub run_multiple_pamenc.sh<br/><br />
<br />
==Checking Data==<br />
Data for this paradigm are logged on the HTAC data base. To get access contact Stone Shih or Fred Sabb.<br />
<br />
=Publications=<br />
<br />
----<br />
Link back to [[LA5C]] page.</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8482HTAC Database - Cleaned Data: Cleaning Rules2013-09-09T21:29:39Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description [see here: [[HTAC]]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and examine the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under complete task descriptions [see here: [[HTAC]]]. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
** 10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
* If SR_PC is less than or equal to 0.50, exclude. <br/><br />
** 11384 <br />
1256 with usable SR summary data. <br/><br />
<br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection, which need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
* If RK_PC is less than or equal to 0.50, exclude. <br/><br />
** 10194 10201 10249 10251 10294 10298 10570 10603 10646 10672 10832 10944 11062 11188 11262 11264 11365 11390 11401 11510 11534 50041 50063 50073 50080 60043 60070 60077 70013 70037 70073 70076<br />
1026 with usable RK summary data. <br/><br />
<br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
* If SMNM_MAIN_PC is less than 0.50, exclude. <br/><br />
** all overlap with subjects identified above<br />
* If SMNM_MANIP_PC is less than 0.50, exclude. <br/><br />
** all overlap with subjects identified above<br />
1213 with usable SMNM summary data. <br/><br />
<br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
* If VMNM_MAIN_PC is less than 0.50, exclude. <br/><br />
** all overlap with subjects identified above<br />
* If VMNM_MANIP_PC is less than 0.50, exclude. <br/><br />
** all overlap with subjects identified above<br />
1210 with usable VMNM summary data. <br/><br />
<br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8481HTAC Database - Data Download Guide2013-06-17T19:05:20Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
* For Step 5, you have two options:<br/><br />
:* '''"CLEANED":''' Cleaning rules have been applied to derived variables, such that subjects that fail cleaning rules for a given task will be excluded. See [[HTAC Database - Cleaned Data: Cleaning Rules]].<br />
:* '''"UNCLEANED":''' Cleaning rules have not been applied to the data, and the investigator should apply them before examining/analyzing the data. See [[HTAC Database - Cleaned Data: Cleaning Rules]].<br />
<br />
''At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable.'' <br/><br />
<br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
'''Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:''' <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
As of 3/7/13, no additional changes should be made to subjects' status, which would alter the Master Set and Population Stratified Set. If any additional changes are agreed to by the exec committee, they should be documented here:<br />
<br />
Update on 6/11/13: Cleaning rules were applied within the database, such that data can now be downloaded as Cleaned Data. See [[HTAC Database - Cleaned Data: Cleaning Rules]]<br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8480HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:18:34Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description [see here: [[HTAC]]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and examine the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under complete task descriptions [see here: [[HTAC]]]. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
** 10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection, which need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8479HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:17:14Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description [see here: [[HTAC]]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and examine the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under complete task descriptions [see here: [[HTAC]]]. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
** 10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8478HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:15:04Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description [see here: [[HTAC]]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and examine the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under complete task descriptions [see here: [[HTAC]]]. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8477HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:14:20Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description [see here: [[HTAC]]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and examine the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under complete task descriptions [see here: [[HTAC]]]. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8476HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:13:32Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description [see here: [[HTAC]]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and examine the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8475HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:13:05Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description [see here: [[HTAC]]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8474HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:12:35Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Cleaned''' or '''Uncleaned''' data. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
<br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8473HTAC Database - Data Download Guide2013-06-11T19:11:42Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
''At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable.'' <br/><br />
<br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
'''Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:''' <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
As of 3/7/13, no additional changes should be made to subjects' status, which would alter the Master Set and Population Stratified Set. If any additional changes are agreed to by the exec committee, they should be documented here:<br />
<br />
Update on 6/11/13: Cleaning rules were applied within the database, such that data can now be downloaded as Cleaned Data. See [[HTAC Database - Cleaned Data: Cleaning Rules]]<br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8472HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:10:59Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under CNP_ANT resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/><br />
<br />
'''ePrime: RL''' [see [[CNP_RL]]] <br/><br />
* No rules to apply. <br/><br />
1265 with usable RL summary data. <br/><br />
<br />
'''ePrime: DRLT''' [see [[CNP_DRL]]] <br/><br />
* If DRLT_EXPERIMENTNAME = "DRLT" or "DRLT_SP_DEVI" or "DRLT_SP_SHIVA", exclude. <br/><br />
** 53 subjects <br/><br />
* If DRLT_POST_TRIAL_TOTAL equals 0, exclude. <br/><br />
** 11019 <br/><br />
517 with usable DRLT summary data. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8471HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T19:06:38Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* '''The Uncleaned Data''' will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* '''The Cleaned Data''' will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under [[CNP_ANT]]] resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/><br />
<br />
'''ePrime: SR''' [see [[CNP_SR]]] <br/><br />
* If SR_ACC_ENC is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
* If SR_ACC_REC is less than or equal to 0.50, exclude. <br/><br />
** 10457, 10928, 10938, 10974, 11042, 11122, 11145, 11290, 11402, 50085 <br/><br />
1257 with usable SR summary data. <br/><br />
<br />
'''ePrime: RK''' [see [[CNP_RK]]] <br/><br />
* If RK_RRESPONSE and RK_KRESPONSE both equal 0, exclude. <br/><br />
** 112 subjects <br/><br />
*** Note about this one: the initial data collected didn't actually collect responses, so 0 scores for these 112 subjects reflect incomplete data collection and need to be excluded. <br/><br />
* If RK_NSI does not equal 60, exclude. <br/><br />
** 101 subjects (9 overlap with the above criterion, so 92 additional subjects excluded based on the RK_NSI rule). <br/><br />
1058 with usable RK summary data. <br/><br />
<br />
'''ePrime: SMNM''' [see [[CNP_SMNM]]] <br/><br />
* If SMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10489, 70003 <br/><br />
* If SMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10014, 10017, 10019, 10044, 10083, 10245, 10311, 10334, 10348, 10382, 10396, 10421, 10462, 10479, 10503, 10683, 10724, 10731, 10769, 10887, 10903, 10986, 11064, 11172, 11243, 11289, 11329, 11344, 11348, 11404, 11407, 11414, 11444, 50033, 50052, 50065, 50069, 60010, 60019, 60022, 60084, 70001, 70014 <br/><br />
* If SMNM_MAIN_MN is less than 0.50, exclude. <br/><br />
** 10019, 10311, 10579, 10724, 10861, 11024, 11243, 50014, 50029, 50033, 50044, 70013 <br/><br />
* If SMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11296, 11365, 50075 <br/><br />
* If SMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 11024, 11365, 50029, 50075 <br/><br />
1213 with usable SMNM summary data. <br/><br />
<br />
'''ePrime: VMNM''' [see [[CNP_VMNM]]] <br/><br />
* If VMNM_TRIALCOUNT is less than 40, exclude. <br/><br />
** 10396, 10537, 60006 <br/><br />
* If VMNM_MANIP_MN is less than 0.50, exclude. <br/><br />
** 10026, 10032, 10038, 10079, 10222, 10416, 10544, 10687, 10699, 10792, 10799, 10848, 10898, 10956, 11050, 11054, 11111, 11299, 11384, 11399, 11414, 11426, 11468, 11540, 50004, 50006, 50022, 50042, 50057, 50077, 60045, 60052, 60068, 60078, 60080, 70020, 70026 <br/><br />
* If VMNM_MAIN_MN is less than 0.50, exclude. <br/> <br />
** 10014, 10026, 10079, 10251, 10331, 10479, 10687, 11111, 11289, 50004, 50016, 50038, 50044, 50057, 70001 <br/><br />
* If VMNM_MANIP_TT is less than 10, or missing, exclude. <br/><br />
** 11003, 11306, 11316, 11551, 50029 <br/><br />
* If VMNM_MAIN_TT is less than 10, or missing, exclude. <br/><br />
** 50016, 50029, 50032 <br/><br />
1211 with usable VMNM summary data. <br/></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8470HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T18:53:09Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* The Uncleaned Data will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* The Cleaned Data will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: DDT''' [see [[CNP_DDT]]] <br/><br />
* If DDT_SMALL_INCON is "Y", exclude. <br/><br />
** 10500, 10667, 11345, 11406 <br/><br />
* If DDT_MEDIUM_INCON is "Y", exclude. <br/><br />
** 10311, 10580, 10756, 10832, 10889, 10932, 11406, 11512, 50004, 50007 <br/><br />
* If DDT_LARGE_INCON is "Y", exclude. <br/><br />
** 10019, 10286, 10792, 11345, 50004, 50007, 60006 <br/><br />
1253 with usable DDT summary data. <br/><br />
<br />
'''ePrime: BART''' [see [[CNP_BART]]] <br/><br />
* If BART_TRIALCOMP is less than 40, exclude. <br/><br />
** None <br/><br />
* If BART_CASHOUTWOPUMP is greater than or equal to 3, exclude. <br/><br />
** 10194, 10228, 10245, 10390, 10432, 10579, 10679, 10702, 10723, 10813, 10843, 10942, 10963, 10998, 11109, 11176, 11257, 11334, 11401, 50004, 50044, 50047, 50057, 60057, 60077, 60079, 70010, 70086 <br/><br />
* If BART_REDEXPLOSIONS is greater than or equal to 19, exclude. <br/><br />
10051, 10575, 10993, 11472, 11528 <br/><br />
1238 with usable BART summary data. <br/><br />
<br />
'''ePrime: ANT''' [see [[CNP_ANT]]] <br/><br />
* None of the rules listed under [[CNP_ANT]]] resulted in the exclusion of a subject, so nothing to apply to the data. <br/><br />
1268 with usable ANT summary data. <br/><br />
<br />
'''ePrime: CPT''' [see [[CNP_CPT]]] <br/><br />
* If CPT_HITS is less than 162, exclude. <br/><br />
** 10416 <br/><br />
1269 with usable CPT summary data. <br/><br />
<br />
'''ePrime: SCWT''' [see [[CNP_SCWT]]] <br/><br />
* If SCWT_ACCCON is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10884, 10985, 11365, 11499, 50004 <br/><br />
* If SCWT_ACCINC is less than or equal to 0.50, exclude. <br/><br />
** 10001, 10112, 10843, 10884, 10985, 11123, 11224, 11301, 11365, 50003, 50004, 50008 <br/><br />
1258 with usable SCWT summary data. <br/><br />
<br />
'''ePrime: SST''' [see [[CNP_Stop_Signal]]] <br/><br />
* If SST_BK1_ENDTRIAL or SST_BK2_ENDTRIAL is less than 128, exclude. <br/><br />
** None <br/><br />
* If SST_SES_SSRT_QUANT is missing (empty field), exclude. <br/><br />
** 10327, 10579, 10855, 10861, 10932, 10986, 11035, 11198 <br/><br />
* If SST_SES_PERCENT_INHIB is less than 0.25 or greater than 0.75, or missing, exclude. <br/><br />
** 10016, 10973, 11022, 11072, 11110, 11243, 11321, 11489, 11503, 50076 <br/><br />
1251 with usable SST summary data. <br/></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8469HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T18:39:06Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* The Uncleaned Data will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* The Cleaned Data will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. Subjects that have been excluded based on cleaning rules will have empty entries for only that task; they will look the same as those subjects that did not complete the task. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/><br />
<br />
'''ePrime: SCAP''' [see [[CNP_SCAP]]] <br/><br />
* If SCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** 10476 <br/><br />
* If SCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 11419, 50004, 50063 <br/><br />
* If SCAP1_CORRECTRT_MEAN, SCAP3_CORRECTRT_MEAN, SCAP5_CORRECTRT_MEAN, or SCAP7_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** 10332, 11419, 50075 <br/><br />
1263 with usable SCAP summary data. <br/><br />
<br />
'''ePrime: VCAP''' [see [[CNP_VCAP]]] <br/><br />
* If VCAP_TRIAL_COUNT does not equal 48, exclude. <br/><br />
** None <br/><br />
* If VCAP_AVERAGE_CORR is less than or equal to 0.50, exclude. <br/><br />
** 10251, 10894, 50004, 50044, 50076, 70037 <br/><br />
* If VCAP3_CORRECTRT_MEAN, VCAP5_CORRECTRT_MEAN, VCAP7_CORRECTRT_MEAN, or VCAP9_CORRECTRT_MEAN is greater than 6000, exclude. <br/><br />
** None <br/><br />
1266 with usable SCAP summary data. <br/></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8468HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T18:31:59Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* The Uncleaned Data will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* The Cleaned Data will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
Details about the cleaning rules, the variables used, and the task are provided under a description of each task. Below, I have listed each cleaning rule applied to the LA2K data for the Cleaned Data option. Listed under each rule are the subject IDs that are excluded based on that cleaning rule, as well the final N of usable data for each task, after cleaning. Note this was conducted using the N = 1316 Population Stratification dataset, so numbers reflect this dataset. <br/><br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]] <br/><br />
* If TRIALCOUNT does not equal 192, exclude. <br/><br />
** None <br/><br />
* If TS_ACCURACY is less than or equal to 0.50, exclude. <br/><br />
** 50004 <br/><br />
1267 with usable TS summary data. <br/></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8467HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T18:26:48Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* The Uncleaned Data will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* The Cleaned Data will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
'''ePrime: TS''' [see [[CNP_TS]]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8466HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T18:26:17Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* The Uncleaned Data will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* The Cleaned Data will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
'''ePrime: TS''' see</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8465HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T18:26:07Z<p>Elizac: </p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either '''Uncleaned''' or '''Cleaned''' data. <br/><br />
* The Uncleaned Data will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* The Cleaned Data will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]]. If you want to download data that is ready for analysis, you would select this option -- although you should still go through and check the data. <br/><br />
<br />
'''ePrime: TS"' see</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Cleaned_Data:_Cleaning_Rules&diff=8464HTAC Database - Cleaned Data: Cleaning Rules2013-06-11T18:22:54Z<p>Elizac: Created page with 'The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/> In The HTAC Customized Data Export section, you can request either Uncleaned or…'</p>
<hr />
<div>The following applies primarily to the E-Prime summary task data (not trial-by-trial data). <br/><br />
<br />
In The HTAC Customized Data Export section, you can request either Uncleaned or Cleaned data. <br/><br />
* The Uncleaned Data will include all summary scores that have been created by the scoring of the raw trial-by-trial data, but which have not been cleaned to exclude subjects that have invalid or incomplete data. If you don't agree with the cleaning rules outlined below, or want to test out new cleaning rules, you would select this option. <br/><br />
* The Cleaned Data will include all summary scores for subjects with usable task data. Although you may conduct additional cleaning of the data (e.g., exclude outliers after examining the distribution of a given variable), these data do not include subjects that failed certain criteria, as outlined under each task description, here [[HTAC]].<br/></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC&diff=8463HTAC2013-06-11T18:16:53Z<p>Elizac: /* Downloading CNP Data */</p>
<hr />
<div>==CNP HTAC Organizational Wiki==<br />
<br />
This Wiki is a collaborative site for interaction with the CNP HTAC dataset, primarily the LA2k data. We hope that many people will contribute, and this site can make the HTAC database a very well organized and well document data resource. Questions about this site can be directed to me,<br />
<br />
thanks<br />
<br />
Fred<br />
<br />
===Bootcamp Slides===<br />
<br />
* [[bootcamp_April14| From April 14th ]]<br />
<br />
===CNP Who to bug and FAQ===<br />
Decision tree for open questions about LA2k<br />
<br />
* Check the [[HTAC_FAQ]] for an answer<br />
* Post a question to the [[HTAC_FAQ]]<br />
* Check the HTAC database/codebook for questions about variables/tests at http://npistat.org/htac/<br />
* If there is a question about statistics or analysis, direct your questions to Bob and Catherine Sugar<br />
* If your question is about the database, direct your questions to Catherine Sugar<br />
* If your question is about details about the execution of the HTAC, direct your questions to Bob<br />
* If your question is about the screening, inclusion/exclusion criteria, or participants, direct your questions to Bob<br />
* If your question is about any [[MM measures]], direct your question to Bob and Ty<br />
* If your question is about any [[RI measures]], direct your questions to Bob and Eydie<br />
* If you've made it this far and still havent emailed someone, you can ask Fred who to email. <br />
<br />
===CNP Publication and Query Guidelines===<br />
<br />
* [[LA2k manual]]<br />
* [[CNP publication policy]]<br />
* [[CNP data query policy]]<br />
<br />
===Downloading CNP Data===<br />
<br />
* [[HTAC Database - Data Download Guide]]<br />
<br />
* [[HTAC Database - Cleaned Data: Cleaning Rules]]<br />
<br />
===LA2K Standardized Task Methods and Parameters===<br />
<br />
* [[CNP_Stop_Signal]] <br />
* [[CNP_BART]] <br />
* [[CNP_DRL]] <br />
* [[CNP_RL]] <br />
* [[CNP_DDT]] <br />
* [[CNP_CPT]] <br />
* [[CNP_TS]] <br />
* [[CNP_SCWT]] <br />
* [[CNP_ANT]] <br />
* [[CNP_SCAP]] <br />
* [[CNP_VCAP]] <br />
* [[CNP_SMNM]] <br />
* [[CNP_VMNM]]<br />
* [[CNP_RK]] <br />
* [[CNP_SR]]<br />
<br />
===LA2K Procedure and Sample Characteristics===<br />
* [[Patient Medication Status]]<br />
<br />
===[[LA5C]]===<br />
* [[LA5C#Participants | Participants]]<br />
* [[LA5C#Contact_People | Contact People]]<br />
* [[LA5C#General_Methods | General Methods]]<br />
* [[LA5C#Facilities | Facilities]]<br />
* [[LA5C#Measures | Measures]]<br />
:'''Functional Data:'''<br/><br />
:*[[BART]]<br/><br />
:*[[BREATH HOLDING]]<br/><br />
:*[[PAM]]<br/><br />
:*[[RESTING STATE]]<br/><br />
:*[[SCAP]]<br/><br />
:*[[STOPSIGNAL]]<br/><br />
:*[[TASKSWITCHING]]<br/><br />
<br />
:'''Structural Data:'''<br/><br />
:*[[DTI]]<br/><br />
:*[[MATCHED BANDWIDTH HIRES]]<br/><br />
:*[[MPRAGE]]<br/><br />
:*[[TASKSWITCHING]]<br/><br />
* [[LA5C#Requesting_and_Analyzing_Data | Requesting and Analyzing LA5C Data]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=TASKSWITCHING&diff=8447TASKSWITCHING2013-05-09T23:09:18Z<p>Elizac: /* Task Background Info */</p>
<hr />
<div>==TASK-SWITCHING TASK==<br />
<br />
==Task Background Info==<br />
<br />
'''Sample Text'''<br />
<br />
In the Task-Switching (TS) task, participants were presented with a series of one of four possible stimuli and asked to respond to the stimulus based on the task cue presented prior to, and above, the image. The four stimuli included a red triangle, red circle, green triangle, and green circle. Participants switched between responding to the image’s color (i.e., red or green) or the shape (i.e., triangle or circle). Cues presented included either “SHAPE” or “S” on trials where participants were expected to respond to the shape of the stimulus; cues presented included either “COLOR” or “C” on trials where participants were expected to respond to the color of the stimulus. On 33% of trials the instructions switched, such that participants were instructed to switch from responding from shape to color, or vice versa. On 67% of trials, the instructions remained the same but the cues changes. This task is designed to measure the changes in reaction time between trials requiring vs. not requiring a switch in responding.<br />
<br />
Participants completed a total of 96 trials, for a total run time of 6 min 52 s.<br />
<br />
For the practice run, participants completed 10 trials of the task. Before reading task instructions, experimenters would mention to participants that this task is difficulty for people, and that a lot of time people need to redo the demo more than once in order to get comfortable with the task, in order to reduce frustration on behalf of the participants. As the button order was counterbalanced across participants, instructions below represent one such button order (and were switched for other participants).<br />
<br />
==Task Instructions==<br />
'''Demo Instructions'''<br />
<br />
This is a demo of the task in which you will learn to press the LEFT (blue) button or the RIGHT (yellow) button in response to color and shapes.<br />
The colors will be red or green. The shapes will be circles or triangles.<br />
Each trial starts with a cue indicating whether you should respond to color or shape. Try to respond as fast and as accurately as you can for each trial.<br />
<br />
If the cue is “COLOR” or “C” respond to the color. Press the LEFT button if the color is RED. Press the RIGHT button if the color is GREEN.<br />
If the cue is “SHAPE” or “S” respond to the shape. Press the LEFT button if the shape is a TRIANGLE. Press the RIGHT button if the shape is a CIRCLE.<br />
<br />
So, again, if the cue is “COLOR” or “C”: <br />
Press the LEFT button if the color is RED.<br />
Press the RIGHT button if the color is GREEN.<br />
If the cue is “SHAPE” or “S”:<br />
Press the LEFT button if the shape is a TRIANGLE. <br />
Press the RIGHT button if the shape is a CIRCLE.<br />
<br />
'''Scan Instructions'''<br />
This task is the one where you have to respond to the color or the shape of images we show you on the screen as fast and as accurately as possible. At the top, you’ll see a cue that tells you what to respond to. If the cue says “COLOR” or “C,” you should respond to the color, and if it says “SHAPE” or “S” you should respond to the shape. When it says to respond to color, press the first button if it’s red, and the second button if it’s green. When it says to respond to shape, push the first button if it’s a triangle, and the second button if it’s a circle. Again, that’s red for button 1 and green for button 2, and triangle for button 1 and circle for button 2. Any questions?<br />
<br />
'''Participants saw on the screen'''<br />
When cued with “S” or “Shape,” respond to the shape of the image.<br />
FIRST button = Circle<br />
SECOND button = Triangle<br />
<br />
When cued with “C” or “Color,” respond to the color of the image.<br />
FIRST button = Red<br />
SECOND button = Green<br />
<br />
Respond as quickly and as accurately as possible.<br />
<br />
==Scoring Behavioral Data AND Creating Onset Files==<br />
/space/raid2/data/poldrack/CNP/scripts/behav_analyze/TASKSWITCH<br/><br />
<br />
There is just one TS scoring script, which makes onset files and behavioral output at same time. <br/><br />
1. Scripts can either be run so that you do all subjects of a given group at once, or do one subject at a time. There are comments in the code that instruct you to indicate first with group (e.g., CONTROLS) you will be doing. By default, it will run all subjects in the selected group unless you choose to comment in a couple lines and enter in a particular subject's ID (e.g., CNP_10150). <br/><br />
<br />
2. in matlab, run 'TASKSWITCH_onsets_analyze.m' <br/><br />
For BEHAVIORAL DATA, this will either create behavioral output (in SUBJ/behav/TASKSWITCH) for each subject in your group or just the subject you specified. This script creates 2 files: <br/><br />
a. It adds to the 'GROUP_TASKSWITCH_group_output.txt' file, by adding a line for each subject with their summary scores. <br/><br />
b. It creates a 'SUBJ_TASKSWITCH_behav_output.txt' file in the subject's behav/TASKSWITCH directory (as well as a trial-by-trial summary file). <br/><br />
The file created under 2a will be used for analysis of behavioral data collected during the TS scan. In most cases, this data will be provided after query. <br/><br />
For ONSET FILES: <br/><br />
a. The following onset files will be created in SUBJ/behav/TASKSWITCH, either for each subject in your group or just the subject you specified. <br/><br />
'SUBJ_CongNSlong_ons.txt' <br/><br />
'SUBJ_CongNSshort_ons.txt' <br/><br />
'SUBJ_CongSlong_ons.txt' <br/><br />
'SUBJ_CongSshort_ons.txt' <br/><br />
'SUBJ_InconNSlong_ons.txt' <br/><br />
'SUBJ_InconNSshort_ons.txt' <br/><br />
'SUBJ_InconSlong_ons.txt' <br/><br />
'SUBJ_InconSshort_ons.txt' <br/><br />
Each onset file is in the same 3-column format as other tasks, with Onset time, Duration, and Weight--with Duration = RT on that trial, and Weight fixed at 1 for all trials. <br/><br />
<br />
b. The script also creates a 'GROUP_TASKSWITCH_log.txt' file that flags any subject with an empty onset file for a given trial type. In this case, the subject would need to be flagged. <br/><br />
<br />
==Running First Level Analyses==<br />
/space/raid2/data/poldrack/CNP/scripts/run_level1_scripts/TASKSWITCH<br />
<br />
1. There are two scripts for the two TASKSWITCH models, although model2 is favored now. <br/><br />
There are separate scripts for each GROUP (e.g., 'run_level1_TASKSWITCH_CONTROLS.sh'). <br/><br />
<br />
Each of these scripts creates an individualized .fsf file for each subject (which is then stored in /space/raid2/data/poldrack/CNP/scripts/designs/TASKSWITCH), then runs it, and the output is stored in the subject's analysis/TASKSWITCH directory. <br/><br />
<br />
2. To run each script, open it in emacs. <br/><br />
a. As is commented into the script, you need to comment in certain sections depending on whether the subject was scanned at BMC vs. CCN. This is in two places, at the top and bottom of the script. <br/><br />
b. Enter in the subject(s) ID(s) on a new line of code (e.g., 'for id in 11131, do'). <br/><br />
c. Save and submit to grid to run. <br/><br />
<br />
==List of Models==<br />
level1_TASKSWITCH_model1 <br/><br />
level1_TASKSWITCH_model2 (this is the preferred model now) <br/><br />
<br />
==Model description and contrasts==<br />
[[TS model1 detail]] <br/><br />
[[TS model2 detail]]<br />
<br />
==Behavioral Variables==<br />
<br />
-- INTERNAL CHECKS <br/><br />
1. Task total CongShort trials <br/><br />
2. Task total CongLong trials <br/><br />
3. Task total InconShort trials <br/><br />
4. Task total InconLong trials <br/><br />
5. Task total NSshort trials <br/><br />
6. Task total Sshort trials <br/><br />
7. Task total NSlong trials <br/><br />
8. Task total Slong trials <br/><br />
-- OVERALL <br/><br />
9. button set <br/><br />
10. Task total correct <br/><br />
11. Task Percent Correct responses <br/><br />
12. Task mean RT <br/><br />
13. Task SD RT <br/><br />
-- The following 5 variables (A-E) <br/><br />
A. Total number of trials <br/><br />
B. Total Correct <br/><br />
C. Total Percent Correct responses <br/><br />
D. Mean RT <br/><br />
E. SD RT <br/><br />
For each of the following Categories of trial types: <br/><br />
Congruent (14-18) <br/><br />
Incongruent (19-23) <br/><br />
Switch (24-28) <br/><br />
NoSwitch (29-33) <br/><br />
CSI Delay-Short (34-38) <br/><br />
CSI Delay-Long (39-43) <br/><br />
Congruent Switch Short Delay (44-48) <br/><br />
Congruent NoSwitch Short Delay (49-53) <br/><br />
Congruent Switch Long Delay (54-58) <br/><br />
Congruent NoSwitch Long Delay (59-63) <br/><br />
Incongruent Switch Short Delay (64-68) <br/> <br />
Incongruent NoSwitch Short Delay (69-73) <br/><br />
Incongruent Switch Long Delay (74-78) <br/><br />
Incongruent NoSwitch Long Delay (79-83) <br/><br />
<br />
<br />
----<br />
Link back to [[LA5C]] page.</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC&diff=8446HTAC2013-05-09T23:09:01Z<p>Elizac: /* LA5C */</p>
<hr />
<div>==CNP HTAC Organizational Wiki==<br />
<br />
This Wiki is a collaborative site for interaction with the CNP HTAC dataset, primarily the LA2k data. We hope that many people will contribute, and this site can make the HTAC database a very well organized and well document data resource. Questions about this site can be directed to me,<br />
<br />
thanks<br />
<br />
Fred<br />
<br />
===Bootcamp Slides===<br />
<br />
* [[bootcamp_April14| From April 14th ]]<br />
<br />
===CNP Who to bug and FAQ===<br />
Decision tree for open questions about LA2k<br />
<br />
* Check the [[HTAC_FAQ]] for an answer<br />
* Post a question to the [[HTAC_FAQ]]<br />
* Check the HTAC database/codebook for questions about variables/tests at http://npistat.org/htac/<br />
* If there is a question about statistics or analysis, direct your questions to Bob and Catherine Sugar<br />
* If your question is about the database, direct your questions to Catherine Sugar<br />
* If your question is about details about the execution of the HTAC, direct your questions to Bob<br />
* If your question is about the screening, inclusion/exclusion criteria, or participants, direct your questions to Bob<br />
* If your question is about any [[MM measures]], direct your question to Bob and Ty<br />
* If your question is about any [[RI measures]], direct your questions to Bob and Eydie<br />
* If you've made it this far and still havent emailed someone, you can ask Fred who to email. <br />
<br />
===CNP Publication and Query Guidelines===<br />
<br />
* [[LA2k manual]]<br />
* [[CNP publication policy]]<br />
* [[CNP data query policy]]<br />
<br />
===Downloading CNP Data===<br />
<br />
* [[HTAC Database - Data Download Guide]]<br />
<br />
===LA2K Standardized Task Methods and Parameters===<br />
<br />
* [[CNP_Stop_Signal]] <br />
* [[CNP_BART]] <br />
* [[CNP_DRL]] <br />
* [[CNP_RL]] <br />
* [[CNP_DDT]] <br />
* [[CNP_CPT]] <br />
* [[CNP_TS]] <br />
* [[CNP_SCWT]] <br />
* [[CNP_ANT]] <br />
* [[CNP_SCAP]] <br />
* [[CNP_VCAP]] <br />
* [[CNP_SMNM]] <br />
* [[CNP_VMNM]]<br />
* [[CNP_RK]] <br />
* [[CNP_SR]]<br />
<br />
===LA2K Procedure and Sample Characteristics===<br />
* [[Patient Medication Status]]<br />
<br />
===[[LA5C]]===<br />
* [[LA5C#Participants | Participants]]<br />
* [[LA5C#Contact_People | Contact People]]<br />
* [[LA5C#General_Methods | General Methods]]<br />
* [[LA5C#Facilities | Facilities]]<br />
* [[LA5C#Measures | Measures]]<br />
:'''Functional Data:'''<br/><br />
:*[[BART]]<br/><br />
:*[[BREATH HOLDING]]<br/><br />
:*[[PAM]]<br/><br />
:*[[RESTING STATE]]<br/><br />
:*[[SCAP]]<br/><br />
:*[[STOPSIGNAL]]<br/><br />
:*[[TASKSWITCHING]]<br/><br />
<br />
:'''Structural Data:'''<br/><br />
:*[[DTI]]<br/><br />
:*[[MATCHED BANDWIDTH HIRES]]<br/><br />
:*[[MPRAGE]]<br/><br />
:*[[TASKSWITCHING]]<br/><br />
* [[LA5C#Requesting_and_Analyzing_Data | Requesting and Analyzing LA5C Data]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8445HTAC Database - Data Download Guide2013-03-07T20:03:52Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
''At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable.'' <br/><br />
<br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
'''Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:''' <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
As of 3/7/13, no additional changes should be made to subjects' status, which would alter the Master Set and Population Stratified Set. If any additional changes are agreed to by the exec committee, they should be documented here:<br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8444HTAC Database - Data Download Guide2013-03-07T20:03:42Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
''At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable.'' <br/><br />
<br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
'''Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:''' <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
As of 3/7/13, no additional changes should be made to subjects' status, which would alter the Master Set and Population Stratified Set. If any additional changes are agreed to by the exec committee, they should be documented here:<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=A_table_listing_these_subjects_with_a_Flag_is_here.&diff=8443A table listing these subjects with a Flag is here.2013-03-07T19:56:42Z<p>Elizac: </p>
<hr />
<div>[[File:CNP_MasterSet_Flag_Fields1_030713.png]]<br />
[[File:CNP_MasterSet_Flag_Fields2_030713.png]]<br />
<br />
go back to [[HTAC Database - Data Download Guide]] <br/><br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=File:CNP_MasterSet_Flag_Fields2_030713.png&diff=8442File:CNP MasterSet Flag Fields2 030713.png2013-03-07T19:56:19Z<p>Elizac: </p>
<hr />
<div></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=File:CNP_MasterSet_Flag_Fields1_030713.png&diff=8441File:CNP MasterSet Flag Fields1 030713.png2013-03-07T19:56:06Z<p>Elizac: </p>
<hr />
<div></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8440HTAC Database - Data Download Guide2013-03-07T19:51:46Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
''At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable.'' <br/><br />
<br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
'''Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:''' <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8439HTAC Database - Data Download Guide2013-03-07T19:51:26Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
'''Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:''' <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8438HTAC Database - Data Download Guide2013-03-07T19:50:58Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
'''"Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:"''' <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8437HTAC Database - Data Download Guide2013-03-07T19:50:47Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
<br />
"Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:" <br/><br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=File:CNP_FinalSamples_030713.png&diff=8436File:CNP FinalSamples 030713.png2013-03-07T19:49:17Z<p>Elizac: uploaded a new version of "File:CNP FinalSamples 030713.png"</p>
<hr />
<div></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8435HTAC Database - Data Download Guide2013-03-07T19:35:16Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
"Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:"<br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
[[File:CNP FinalSamples 2 030713.png]]<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=File:CNP_FinalSamples_2_030713.png&diff=8434File:CNP FinalSamples 2 030713.png2013-03-07T19:34:17Z<p>Elizac: </p>
<hr />
<div></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8433HTAC Database - Data Download Guide2013-03-07T19:25:34Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
"Notes about Additional Cleaning based on DropDates, DQ, SF and Flags:"<br />
The Master Set is available for download and analysis, and all subjects have been determined to be usable. No subjects *need* to be further excluded based on a DQ or Flag, as explained above. After downloading the Master Set, if an investigator wants to do further filtering based on DQ or Flags, this is the investigator's decision and can be done by sorting based on those fields. <br/><br />
Whether to include or exclude based on Flags is up to the person analyzing the data, just as it is the person's responsibility to make sure that they include subjects with complete data for their measure(s) of interest, that those data have been checked, cleaned, etc. <br/><br />
If the investigator decides to conduct further filtering based on DQ or Flags, this information should be recorded in order to communicate with other investigators and replicate the analyses. <br/><br />
<br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8432HTAC Database - Data Download Guide2013-03-07T19:14:49Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields:''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags:''' <br/><br />
These subjects may have values entered in the Flag field, which is a variable listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags record information that an investigator may want to use to further filter out subjects (specific to the question being asked) or simply record additional information about the subject/data. <br />
<br />
<br />
are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8431HTAC Database - Data Download Guide2013-03-07T19:12:12Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the Flag field. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8430HTAC Database - Data Download Guide2013-03-07T19:12:01Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
*'''Master Set (N = 1254), all patients and controls:''' There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
<br />
'''Notes about Flags'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the Flag field. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8429HTAC Database - Data Download Guide2013-03-07T19:10:32Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields''' <br/><br />
These subjects may have values entered in the DropDate, DQ, SF, or Flag fields, which are variables listed in the Patient Registry form. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
go back to [[HTAC]]<br />
'''Notes about Flags'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the Flag field. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8428HTAC Database - Data Download Guide2013-03-07T19:09:59Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields''' <br/><br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the DropDate, DQ, SF, or Flag fields. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
go back to [[HTAC]]<br />
'''Notes about Flags'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the Flag field. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8427HTAC Database - Data Download Guide2013-03-07T19:08:01Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030713.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the DropDate, DQ, SF, or Flag fields. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
go back to [[HTAC]]<br />
'''Notes about Flags'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the Flag field. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=File:CNP_FinalSamples_030713.png&diff=8426File:CNP FinalSamples 030713.png2013-03-07T19:07:35Z<p>Elizac: </p>
<hr />
<div></div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8425HTAC Database - Data Download Guide2013-03-07T19:04:20Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have three options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030613.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the DropDate, DQ, SF, or Flag fields. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
go back to [[HTAC]]<br />
'''Notes about Flags'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the Flag field. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=HTAC_Database_-_Data_Download_Guide&diff=8424HTAC Database - Data Download Guide2013-03-07T19:03:45Z<p>Elizac: </p>
<hr />
<div>'''Note: This guide assumes that you are familiar with the CNP dataset, names of the data subsets (e.g., LA5C), and have access to the HTAC Database.''' <br/><br />
<br />
In the HTAC Customized Data Export section, you can request data organized by Subject Type (Step 3) or Subject Status (Step 4). <br/><br />
* For Step 3, you can chose to download only a certain set of patients, for example; if you want to download the entire dataset, select "ALL SUBJECTS". <br/><br />
* For Step 4, you have 3 options:<br/><br />
:* '''"Master List (N = 1254)":''' This is most likely the option that all users will chose. This includes all subjects with Status = 2 (Complete). <br/><br />
:* '''"Population Stratified Set":''' This includes all subjects with Status = 2 (Complete), plus 62 additional subjects with Status = 0 and Genetic Recovery Case = 1. This larger dataset (N = 1316) will be used for primary genetic analyses only. We have included the additional Genetic Recovery Cases in an attempt to increase our total sample size as much as possible, but they don't necessarily meet inclusion criteria for the Master List. <br/><br />
:* '''"Inactive/Active/Complete (N = 1839)":''' This includes all subjects recorded in the study. This should only be downloaded for QC purposes. This dataset should not be downloaded and used for analyses. <br/><br />
[[File:CNP_FinalSamples_030613.png]]<br />
<br />
At this point, you have a complete data set with subjects that have been determined to be included in the Master or Population Stratified set. They vary in how complete the data are, but they have all been determined to be usable. <br/><br />
<br />
'''Notes about DropDate, DQ, and SF Fields'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the DropDate, DQ, SF, or Flag fields. ''These do not necessarily make the subject unusable.'' <br/><br />
* '''Positive DropDate''': Not a grounds for exclusion, but rather indicate who may have stopped, then restarted the study. Those that were really dropped for meeting exclusion criteria were marked as Inactive (Status = 1) and since these data should not be downloaded for analysis, there should be no confusion between a "real" DQ and the DropDate/DQ fields here. <br/><br />
* '''DQ_Reason''': This variable was used throughout the study to record why a subject did not complete the study. This does not make their data unusable. Many of the remaining DQ_codes were entered at the scanning stage and are therefore scan specific (e.g., subject failed to show up for their scan, so this code was entered in the Registry in order to indicate why this portion of their data are missing). Consistent with the fact that their Status = 2 (Complete), the data are usable, despite a positive DQ code. <br/><br />
* '''SF_Reason''': This was another field used in the Registry to record information about why complete data are not available from the subject. Presence of a SF flag does not mean that the data are unusable. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 6 subjects with DropDates, 17 subjects with a DQ_Reason, and 3 subjects with a SF_Reason. Since many of these overlap, there are 20 total subjects with either of these fields filled in. Whatever data were collected from these subjects has been determined to be usable. [[A table listing these subjects with either DropDate, DQ or SF is here.]] <br/><br />
<br />
go back to [[HTAC]]<br />
'''Notes about Flags'''<br />
Under variables listed in the Patient Registry form, these subjects may have values entered in the Flag field. ''These do not necessarily make the subject unusable.'' <br/><br />
For the most part, Flags are left for investigator decision: it is up to the person conducting the analyses to decide whether or not to exclude any subject with a certain type of flag. If they do remove subjects after downloading the Master Set, it would be helpful to keep track of which subjects are excluded for communicating with other investigators and for replicating the analyses. <br/><br />
<br />
Master Set (N = 1254), all patients and controls: There are 70 subjects with a Flag. [[A table listing these subjects with a Flag is here.]] <br/><br />
<br />
go back to [[HTAC]]</div>Elizachttp://lcni-3.uoregon.edu/phenowiki/index.php?title=A_table_listing_these_subjects_with_a_Flag_is_here.&diff=8423A table listing these subjects with a Flag is here.2013-03-07T19:02:48Z<p>Elizac: </p>
<hr />
<div>[[File:CNP_MasterSet_Flag_Fields1.png]]<br />
[[File:CNP_MasterSet_Flag_Fields2.png]]<br />
<br />
go back to [[HTAC Database - Data Download Guide]] <br/><br />
go back to [[HTAC]]</div>Elizac