Writing a New Check
In sdtmchecks: Data Quality Checks for Study Data Tabulation Model (SDTM) Datasets

Background

Clinical trial datasets can contain a million different types of incorrect data. This package does not intend to comprehensively cover all scenarios in which data may be wrong. Nor does this package intend to replicate the comprehensive set of P21 data checks for SDTM. Instead, the data checks in this package are intended to be generalizable, actionable, and meaningful for analysis. For example many clinical trials contain the CO domain, however the sdtmchecks package does not have any functionality around this domain as it is usually not meaningful for analysis.

Working Collaboratively

GitHub

The main branch (pharmaverse/sdtmchecks@main) contains the latest released version and should not be used for development.

The devel branch is the default branch and contains the latest development version of the package. To start contributing, please make a feature branch off of devel. To install, please refer to the front page of the package site. When your code is ready to be incorporated please open a pull request that another person will review prior to merging the update into devel. If you do not have write access to the repository, please work off of a forked repo and open a pull request from the fork.

Package Dependencies

The {renv} package is used to handle package dependencies. Run renv::restore() to install the same set of package versions being used by the team.

Existing Checks

The sdtmchecksmeta dataset lists existing checks and contains helpful additional information

#Just type this in
sdtmchecksmeta

library(sdtmchecks)
meta<-subset(sdtmchecksmeta, select=c("check","domains","xls_title","pdf_title"))
colnames(meta)<-c("check","domains","title", "description")
head(meta,n=10)

Good Practices

When writing a new data check function, the function name should start with the word 'check' and contain key dataset information plus a 1-2 word description, i.e. check_[dataset1]_..._[datasetN]_[brief description]. For example:
check_dm_race <- function(DM){...}
check_dv_ae_covid <- function(DV, AE){...}
Each function should at minimum take the datasets being investigated as parameters, e.g. check_ae_aedecod <- function(AE){...}
Each function should start with a check for required variables. The following internal utility functions may be helpful for this: %lacks_all%, %lacks_any%, %has_all%, or %has_any%.
All checks should use the internal pass() and fail() functions to return either TRUE or FALSE depending on check results. These functions enable attributes to be attached to the boolean result, e.g. a message or a listing of flagged records.
One convention used by this package is to have a return message for a fail() scenario with accompaying dataframe to have an additional space after the period. For example, "...age value(s). " rather than "...age value(s)." This extra space is a formatting detail used within an existing report.
sdtmchecks intentionally attempts to minimize external dependencies. This is something to keep in mind when developing a new check. Currently the only dependencies within data check functions are dplyr and tidyselect.
Include examples with dummied dataframes in the header that test the logic, assumptions, and robustness of the data check function.
Even better, write unit tests with the testthat package. Existing unit tests are in the subdirectory tests/testthat. The code written for the header examples may have significant overlap with the unit tests.

Example Check

If you are writing your first check it might be helpful to start by editing an existing one, for example the one below:

#' Example check
#'
#' @param DM 
#'
#' @return boolean
#' @export
#'
#' @examples
#'
#' \dontrun{
#'    check_dm_age_missing(DM)
#'   }
#'

check_dm_age_missing <- function(DM){
  ###First check that required variables exist and return a message if they don't
  if(DM %lacks_any% c("USUBJID","AGE")){
      fail(lacks_msg(DM, c("USUBJID","AGE")))
  }else{
    ### Subset DM to only records with missing AGE
    mydf_0 = subset(DM, is_sas_na(DM$AGE), c("USUBJID","AGE"))
    ### Subset DM to only records with AGE<18
    mydf_1 = subset(DM, !is_sas_na(DM$AGE) & DM$AGE<18, c("USUBJID","AGE"))
    ### Subset DM to only records with AGE>90
    mydf_2 = subset(DM, !is_sas_na(DM$AGE) & DM$AGE>=90, c("USUBJID","AGE"))
    ### Combine records with abnormal AGE
    mydf3 = rbind(mydf_0, mydf_1, mydf_2)
    mydf = mydf3[order(mydf3$USUBJID),]
    rownames(mydf)=NULL
    ###Print to report
    ### Return message if no records with missing AGE, AGE<18 or AGE>90
    if(nrow(mydf)==0){
      pass()
      ### Return subset dataframe if there are records with missing AGE, AGE<18 or AGE>90
    }else if(nrow(mydf)>0){
      fail(paste("DM has ",length(unique(mydf$USUBJID)),
                   " patient(s) with suspicious age value(s). ",sep=""),
             mydf)
        }
  }
}

Additional Considerations

Can any variables be considered optional rather than required?
Is enough information returned that identify the issue source? For example, returning only a patient ID may not enable easy identification of the problem records.
Is this check impactful for analysis? Checking little used aspects of SDTM data can cause unnecessary work.
Will this check be meaningful across many trials or is specific to just a small number of trials?
Are false positives being minimized? The check will not be useful if end users need to sift through a large number of incorrectly flagged records to find a one or two correctly flagged records.
Do SDTM IG or MedDRA versions need to be considered?
Please consider including noteworthy details and important considerations in the Roxygen2 header so that information is available in package documentation.