TRR


Welcome to TRR

    Test-Retest Reliability Program through Bayesian Multilevel Modeling
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Version 0.0.3, March 13, 2021
Author: Gang Chen (gangchen@mail.nih.gov)
Website - https://afni.nimh.nih.gov/gangchen_homepage
SSCC/NIMH, National Institutes of Health, Bethesda MD20892
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Usage:

TRR performs test-rest reliability analysis for behavior data as well as
region-based neuroimaging data. If no multiple trials are involved in a
dataset, use the conventional intraclass correlation (ICC) with, for
example, 3dICC for neuroimaging data. However, when there are multiple
trials for each condition, the traditional intraclass correlation may
underestimate TRR to various extent. 3dLMEr could be utilized with the
option -TRR to estimate test-retest reliability with trial-level data for
whole-brain analysis; however, it may only work for data with strong
effects such as a single effect (e.g., one condition or average across
conditions).

The input data for the program TRR have to be at the trial level without
any summarization at the condition level. The TRR estimation is conducted
through a Byesian multilevel model with a shell script (as shown in the
examples below). The input data should be formulated in a pure-text table
that codes all the variables.

Citation:

If you want to cite the modeling approach for TRR, consider the following

Chen G, et al., Beyond the intraclass correlation: A hierarchical modeling
approach to test-retest assessment.
https://www.biorxiv.org/content/10.1101/2021.01.04.425305v1

Read the following carefully!
A data table in pure text format is needed as input for an TRR script. The
data table should contain at least 3 (with a single condition) or 4 (with
two conditions) columns that specify the information about subjects,
sessions and response variable values:

Subj   session    Y
S1      T1     0.2643
S1      T2     0.3762

Subj condition  session    Y
S1    happy      T1     0.2643
S1    happy      T2     0.3762
S1     sad       T1     0.3211
S1     sad       T2     0.3341


0) Through Bayesian analysis, a whole TRR distribution will be presented in
   the end as a density plot in PDF. In addition, the distribution is
   summarized with a mode (peak) and a highest density interval that are
   stored in a text file with a name specified through -prefix with the
   appendix .txt.

1) Avoid using pure numbers to code the labels for categorical variables. The
   column order does not matter. You can specify those column names as you
   prefer, but it saves a little bit scripting if you adopt the default naming
   for subjects ('Subj'), sessions ('sess') and response variable ('Y').

2) Sampling error for the trial-level effects can be incorporated into the
   model. This is especially applicable to neuroimaging data where the trial
   level effects are typically estimated through time series regression with
   GLS (e.g., 3dREMLfit in AFNI); thus, the standard error or t-statistic can
   be provided as part of the input through an extra column in the data table
   and through the option -se in the TRR script.

3) If there are more than 4 CPUs available, one could take advantage of within
   chain parallelization through the option -WCP. However, extra stpes are
   required: both 'cmdstan' and 'cmdstanr' have to be installed. To install
   'cmdstanr', execute the following command in R:
   install.packages('cmdstanr', repos = c('https://mc-stan.org/r-packages/', getOption('repos')))
   Follow the instruction here for the installation of 'cmdstan':
   https://mc-stan.org/cmdstanr/articles/cmdstanr.html
   If 'cmdstan' is installed in directory other than home, use option
   -StanPath to specify the path (e.g., -StanPath '~/my/stan/path').

4) The results from TRR can be slightly different from each execution or
   different computers and R package versions due to the nature of randomness
   involved in Monte Carlo simulations, but the differences should be negligle
   unless numerical failure occurs.

Installation requirements:

In addition to R installation, the R packages "brms", "coda" and "ggplot2" are
required for TRR. Make sure you have a recent version of R. To install these
packages, run the following command at the terminal:

rPkgsInstall -pkgs "brms,coda,ggplot2" -site http://cran.us.r-project.org"

Alternatively you may install them in R:
install.packages("brms")
install.packages("coda")
install.packages("ggplot2")

To take full advantage of parallelization, install both 'cmdstan' and 'cmdstanr'
and use the option -WCP in TRR (see comments above).

Running:

Once the TRR command script is constructed saved as a text file, for example,
called myTRR.txt, execute it with the following (assuming on tcsh shell),

nohup tcsh -x myTRR.txt > diary.txt &
nohup tcsh -x myTRR.txt |& tee diary.txt &

The progression of the analysis is stored in the text file diary.txt and can
be examined later. The 'nohup' command allows the analysis running in the
background even if the terminal is killed.

Examples:

Example 1 --- TRR estimation for a single effect - simple scenario: one
          condition, two sessions. Trial level effects are the input
          from each subject, and test-retest reliability between two sessions is
          the research focus.

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -dataTable myData.tbl   \

   If a computer is equipped with as many CPUs as a factor 4 (e.g., 8, 16, 24,
   ...), a speedup feature can be adopted through within-chain parallelization
   with the option -WCP (and -StanPath). For example, the script assumes a
   computer with 24 CPUs (6 CPUs per chain):

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -WCP 6 -StanPath '~/my/stan/path' -dataTable myData.tbl   \

   If the data are skewed or have outliers, use exGaussian or Student's t:

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -distY exgaussian -dataTable myData.tbl   \

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -distY student -dataTable myData.tbl   \

   The input file 'myData.txt' is a data table in pure text format as below:

     Subj  sess          Y
     S01   sess1      0.162
     S01   sess1      0.212
     S01   sess2     -0.598
     S01   sess2      0.327
     S02   sess1      0.249
     S02   sess1      0.568


Example 2 --- TRR estimation for a contrast between two conditions. Input
   data include trial-level effects for two conditions during two sessions.

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -condition cond -dataTable myData.tbl   \

   A version with within-chain parallelization through option '-WCP 6' on a
   computer with 24 CPUs:

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -condition cond -WCP 6 -StanPath '~/my/stan/path' \
      -dataTable myData.tbl   \

   Another version with the assumption of student t-distribution:

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -condition cond -distY student -dataTable myData.tbl   \

   The input file 'myData.txt' is a data table in pure text format as below:

     Subj  sess   cond       Y
     S01   sess1  C1    0.162
     S01   sess1  C1    0.212
     S01   sess1  C2    0.262
     S01   sess1  C2    0.638
     S01   sess2  C1   -0.598
     S01   sess2  C1    0.327
     S01   sess2  C2    0.249
     S01   sess2  C2    0.568



Example 3 --- TRR estimation for a contrast between two conditions. Input
   data include trial-level effects plus their t-statistic or standard error
   values for two conditions during two sessions.

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -condition cond -tstat tvalue -dataTable myData.tbl   \

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -condition cond -se SE -dataTable myData.tbl   \

   A version with within-chain parallelization through option '-WCP 6' on a
   computer with 24 CPUs:

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -condition cond -tstat tvalue -WCP 6 \
      -StanPath '~/my/stan/path' \
      -dataTable myData.tbl \

   Another version with the assumption of Student t-distribution:

   TRR -prefix myTRR -chains 4 -iterations 1000 -Y RT -subject Subj \
      -repetition sess -condition cond -tstat tvalue -distY student \
      -dataTable myData.tbl   \

   The input file 'myData.txt' is a data table in pure text format as below:

     Subj  sess  cond  tvalue     Y
     S01   sess1  C1    2.315   0.162
     S01   sess1  C1    3.212   0.341
     S01   sess1  C2    1.262   0.234
     S01   sess1  C2    0.638   0.518
     S01   sess2  C1   -2.598  -0.213
     S01   sess2  C1    3.327   0.423
     S01   sess2  C2    4.249   0.791
     S01   sess2  C2    3.568   0.351

Options:

Options in alphabetical order:

   -chains N: Specify the number of Markov chains. Make sure there are enough
         processors available on the computer. Most of the time 4 cores are good
         enough. However, a larger number of chains (e.g., 8, 12) may help achieve
         higher accuracy for posterior distribution. Choose 1 for a single-processor
         computer, which is only practical only for simple models.

   -condition var_name: var_name is used to specify the column name that is
        designated as the condition variable. Currently TRR can only handle
        two conditions. Note that when this option is not invoked, no
        condition variable is assumed to be present, and the TRR analysis
        will proceed with a singl effect instead of a contrast between two
        conditions.

   -cVars variable_list: Identify categorical (qualitive) variables (or
         factors) with this option. The list with more than one variable
         has to be separated with comma (,) without any other characters such
         as spaces and should be surrounded within (single or double) quotes.
         For example, -cVars "sex,site"

   -dataTable TABLE: List the data structure in a table of long format (cf. wide
         format) in R with a header as the first line.

         NOTE:

         1) There should have at least three columns in the table. These minimum
         three columns can be in any order but with fixed and reserved with labels:
         'Subj', 'ROI', and 'Y'. The column 'ROI' is meant to code the regions
         that are associated with each value under the column Y. More columns can
         be added in the table for explanatory variables (e.g., groups, age, site)
         if applicable. Only subject-level (or between-subjects) explanatory variables
         are allowed at the moment. The labels for the columns of 'Subj' and 'ROI'
         can be any identifiable characters including numbers.
         2) Each row is associated with one and only one 'Y' value, which is the
         response variable in the table of long format (cf. wide format) as
         defined in R. With n subjects and m regions, there should have totally mn
         rows, assuming no missing data.
         3) It is fine to have variables (or columns) in the table that are not used
         in the current analysis.
         4) The context of the table can be saved as a separate file, e.g., called
         table.txt. In the script specify the data with '-dataTable table.txt'.
         This option is useful when: (a) there are many rows in the table so that
         the program complains with an 'Arg list too long' error; (b) you want to
         try different models with the same dataset.

   -dbgArgs: This option will enable R to save the parameters in a file called
         .TRR.dbg.AFNI.args in the current directory so that debugging can be
         performed.

   -distY distr_name: Use this option to specify the distribution for the response
        variable. The default is Gaussian when this option is not invoked. When
        skewness or outliers occur in the data, consider adopting the Student's
        t-distribution, exGaussian, log-normal etc. by using this option with
        'student', 'exgaussian', 'lognormal' and so on.

   -help: this help message

   -iterations N: Specify the number of iterations per Markov chain. Choose 1000 (default)
         for simple models (e.g., one or no explanatory variables). If convergence
         problem occurs as indicated by Rhat being great than 1.1, increase the number of
         iterations (e.g., 2000) for complex models, which will lengthen the runtime.
         Unfortunately there is no way to predict the optimum iterations ahead of time.

   -model FORMULA: This option specifies the effects associated with explanatory
         variables. By default (without user input) the model is specified as
         1 (Intercept). Currently only between-subjects factors (e.g., sex,
         patients vs. controls) and quantitative variables (e.g., age) are
         allowed. When no between-subject factors are present, simply put 1
         (default) for FORMULA. The expression FORMULA with more than one
         variable has to be surrounded within (single or double) quotes (e.g.,
         '1+sex', '1+sex+age'. Variable names in the formula should be consistent
         with the ones used in the header of data table. A+B represents the
         additive effects of A and B, A:B is the interaction between A
         and B, and A*B = A+B+A:B. Subject as a variable should not occur in
         the model specification here.

   -PDP width height: Specify the layout of posterior distribution plot (PDP) with
         the size of the figure windown is specified through the two parameters of
         width and height in inches.

   -prefix PREFIX: Prefix is used to specify output file names. The main output is
        a text with prefix appended with .txt and stores inference information
        for effects of interest in a tabulated format depending on selected
        options. The prefix will also be used for other output files such as
        visualization plots, and saved R data in binary format. The .RData can
        be used for post hoc processing such as customized processing and plotting.
        Remove the .RData file to save disk space once you deem such a file is no
        longer useful.

   -qVars variable_list: Identify quantitative variables (or covariates) with
         this option. The list with more than one variable has to be
         separated with comma (,) without any other characters such as
         spaces and should be surrounded within (single or double) quotes.
         For example, -qVars "Age,IQ"

   -repetition var_name: var_name is used to specify the column name that is
        designated as for the repetition variable such as sess<ion. The default
        (when this option is not invoked) is 'repetition'. Currently it only allows
        two repetitions in a test-test scenario.

   -se: This option indicates that standard error for the response variable is
         available as input, and a column is designated for the standard error
         in the data table. If effect estimates and their t-statistics are the
         output from preceding analysis, standard errors can be obtained by
         dividing the effect estimatrs ('betas') by their t-statistics. The
         default assumes that standard error is not part of the input.

   -se: This option indicates that standard error for the response variable is
         available as input, and a column is designated for the standard error
         in the data table. If effect estimates and their t-statistics are the
         output from preceding analysis, standard errors can be obtained by
         dividing the effect estimatrs ('betas') by their t-statistics. The
         default assumes that standard error is not part of the input.

   -show_allowed_options: list of allowed options

   -StanPath dir: Use this option to specify the path (directory) where 'cmdstan' is
         is installed on the computer. Together with option '-WCP', within-chain
         parallelization can be used to speed up runtime. To take advantage of
         this feature, you need the following: 1) at least 8 or more CPUs; 2)
         install 'cmdstan'; 3) install 'cmdstanr'. The default (the absence of the
         option '-StanPath') means that 'cmdstan' is under the home directroy:
         '~/'; otherwise, explicictly indicate the path as, for example,
         '-StanPath "~/here/is/myStanPath/"'.

   -subject var_name: var_name is used to specify the column name that is
        designated as for the subject variable. The default (when this option
        is not invoked) is 'subj'.

   -subject var_name: var_name is used to specify the column name that is
        designated as for the subject variable. The default (when this option
        is not invoked) is 'subj'.

   -tstat var_name: var_name is used to specify the column name that lists
        the t-statistic values, if available, for the response variable 'Y'.
        In the case where standard errors are available for the effect
        estiamtes of 'Y', use the option -se.

   -verb VERB: Speicify verbose level.

   -WCP k: This option will invoke within-chain parallelization to speed up runtime.
         To take advantage of this feature, you need the following: 1) at least 8
         or more CPUs; 2) install 'cmdstan'; 3) install 'cmdstanr'. The value 'k'
         is the number of thread per chain that is requested. For example, with 4
         chains on a computer with 24 CPUs, you can set 'k' to 6 so that each
         chain will be assigned with 6 threads.

   -Y var_name: var_name is used to specify the column name that is designated as
        as the response/outcome variable. The default (when this option is not
        invoked) is 'Y'.