1 Introduction

This document presents an example of standard patient profile report for a typical CDISC ‘Study Data Tabulation Model’ (a.k.a SDTM) datasets.

This document is intended to be used as a template to create patient profile report for your specific study. You can copy the source code of this document into your working directory with:

# get template from the package
pathTemplate <- system.file(
    "doc", "patientProfiles-template-SDTM.Rmd", 
    package = "patientProfilesVis"
)
file.copy(from = pathTemplate, to = ".")
# Note: your current working directory can be checked with: getwd()

This template can then be further tailored to your specific study (changing data path, variables available in the data, …).

The following R packages are required:

    library(patientProfilesVis)

1.1 Dataset

The SDTMs datasets are imported into R.

    library(clinUtils)
    
    # For this vignette, an example STDM dataset is used
    # (subset of the CDISC Pilot 01 study) 
    data(dataSDTMCDISCP01, package = "clinUtils")
    dataAll <- dataSDTMCDISCP01
    labelVars <- attr(dataSDTMCDISCP01, "labelVars")
    
    # If SDTM datasets should be imported from the external folder,
    # the following code could be used:
#   pathFiles <- list.files(path = "/path/to/data", pattern = "*.sas7bdat$", full.names = TRUE)
#   dataAll <- loadDataADaMSDTM(files = pathFiles)
#   labelVars <- attr(dataAll, "labelVars")

    patientProfilesPlots <- list()

2 Creation of patient profiles

For each domain of interest, the subject profiles are created. See the vignette:

2.1 Demographics

    dmPlots <- subjectProfileTextPlot(
        data = dataAll$DM,
        paramValueVar = c(
            "SEX", "RACE", "ETHNIC",
            "COUNTRY", "ARM",
            "AGE", "RFSTDTC"
        ),
        # optional:
        labelVars = labelVars
    )
    patientProfilesPlots <- c(patientProfilesPlots, list(DM = dmPlots))

2.2 Medical history

    mhPlots <- subjectProfileTextPlot(
        data = dataAll$MH,
        paramValueVar = c("MHDECOD", "MHTERM", "MHSTDTC"), #"MHENDTC" if available
        # optional:
        paramGroupVar = c("MHSTDTC", "MHDY"),
        title = "Medical history (Start - End)",
        labelVars = labelVars,
        table = TRUE
    )
    patientProfilesPlots <- c(patientProfilesPlots, list(MH = mhPlots))

2.3 Concomitant medications

    cmPlots <- subjectProfileIntervalPlot(
        data = dataAll$CM,
        paramVar = c(
            "CMTRT", 
            "CMDOSE", "CMDOSU", "CMROUTE", 
            "CMDOSFRQ"
        ),
        timeStartVar = "CMSTDY",
        timeEndVar = "CMENDY",
        # optional:
        paramGroupVar = "CMCLAS", # or CMINDC
        colorVar = "CMCLAS", # or CMINDC
        labelVars = labelVars,
        title = "Concomitant medications",
        # To zoom in axis scale in study time frame
        # (to avoid scale is focused on negative pre-study time frame for CM)
        timeTrans = getTimeTrans(type = "asinh-neg"), 
        alpha = 0.8,
        timeAlign = FALSE
    )

## 171 record(s) with missing Study Day of Start of Medication and 208 record(s) with missing Study Day of End of Medication are imputed with minimal imputation.

    patientProfilesPlots <- c(patientProfilesPlots, list(CM = cmPlots))

2.4 Treatment exposure

    exPlots <- subjectProfileIntervalPlot(
        data = dataAll$EX,
        paramVar = c(
            "EXTRT", "EXDOSE", "EXDOSU", 
            "EXDOSFRM", "EXDOSFRQ", "EXROUTE"
        ), # "EXTPT" if available
        timeStartVar = "EXSTDY",
        timeEndVar = "EXENDY",
        # optional:
        colorVar = "EXDOSFRM",
        title = "Treatment exposure",
        alpha = 0.8,
        labelVars = labelVars
    )
    
    patientProfilesPlots <- c(patientProfilesPlots, list(EX = exPlots))

2.5 Adverse events

    dataAE <- dataAll$AE$AESEV
    # to adapt for specific dataset
    dataAE$AESEV <- factor(dataAll$AE$AESEV, levels = c("MILD", "MODERATE", "SEVERE"))

## Warning in dataAE$AESEV <- factor(dataAll$AE$AESEV, levels = c("MILD",
## "MODERATE", : Coercing LHS to a list

    aePlots <- subjectProfileIntervalPlot(
        data = dataAll$AE,
        paramVar = "AEDECOD", # AETERM, depending on coding
        timeStartVar = "AESTDY",
        timeEndVar = "AEENDY",
        # optional:
        paramGroupVar = "AESOC",
        colorVar = "AESEV", 
        title = "Adverse events",
        timeAlign = FALSE,
        alpha = 0.8,
        labelVars = labelVars
    )

## 3 record(s) with missing Study Day of Start of Adverse Event and 19 record(s) with missing Study Day of End of Adverse Event are imputed with minimal imputation.

    patientProfilesPlots <- c(patientProfilesPlots, list(AE = aePlots))

2.6 Laboratory measurements

    dataLB <- dataAll$LB
    # to adapt for dataset
    dataLB$LBNRIND <- factor(dataLB$LBNRIND, levels = c("LOW", "NORMAL", "HIGH", "ABNORMAL"))

    # specify custom color and shape palette
    colorPaletteLB <- clinUtils::getPaletteCDISC(x = dataLB$LBNRIND, var = "NRIND", type = "color")
    shapePaletteLB <- clinUtils::getPaletteCDISC(x = dataLB$LBNRIND, var = "NRIND", type = "shape")
    
    lbPlots <- subjectProfileLinePlot(
        data = dataLB,
        paramValueVar = "LBSTRESN",
        paramNameVar = "LBTEST",
        timeVar = "LBDY",
        # optional
        paramValueRangeVar = c("LBSTNRLO", "LBSTNRHI"),
        paramGroupVar = "LBCAT", # "LBSCAT" if available
        colorVar = "LBNRIND", colorPalette = colorPaletteLB,
        shapeVar = "LBNRIND", shapePalette = shapePaletteLB,
        shapeSize = 4,
        title = "Laboratory test measurements",
        alpha = 0.8,
        labelVars = labelVars
    )
    
    patientProfilesPlots <- c(patientProfilesPlots, list(LB = lbPlots))

2.7 Vital signs

    # If available, reference range indicator
    # could be displayed via the color/shape variable

    vsPlots <- subjectProfileLinePlot(
        data = dataAll$VS,
        paramValueVar = "VSSTRESN",
        paramNameVar = "VSTEST",
        timeVar = "VSDY",
        # optional
#       paramGroupVar = "VSCAT", # if available
        colorVar = "VSPOS", # if available
        shapeSize = 2,
        title = "Vital signs",
        alpha = 0.8,
        labelVars = labelVars
    )

## Empty records in the: 'VSPOS' variable are converted to NA.

    patientProfilesPlots <- c(patientProfilesPlots, list(VS = vsPlots))

2.8 Electrocardiogram

Note: no ECG data is available for the example dataset, so this domain is not considered for the example.

    # If available, reference range indicator
    # could be displayed via the color/shape variable

    egPlots <- subjectProfileLinePlot(
        data = dataAll$EG,
        paramValueVar = "EGSTRESN",
        paramNameVar = "EGTEST",
        timeVar = "EGDY",
        # optional
        title = "Electrocardiogram",
        alpha = 0.8,
        labelVars = labelVars
    )
    patientProfilesPlots <- c(patientProfilesPlots, list(EG = egPlots))

2.9 Creation of the reports

The subject profile report is created by subject.

Please note that the subject profile reports are not not created by default in the vignette, for time constraints.

Feel free to run yourself the code, and check the resulting pdf reports!

    pathsPatientProfiles <- createSubjectProfileReport(
            
        listPlots = patientProfilesPlots, 
        
        # optional
        reportPerSubject = TRUE, 
        verbose = TRUE,
        outputFile = './patientProfiles/subjectProfile.pdf',
        timeAlign = "all", timeAlignPerSubject = "all",
        exportBatchSize = 5,
        
        # export subjects with highest adverse events severity
        subjectSortData = dataAll$AE,
        subjectSortVar = "AESEV",
        subjectSortDecreasing = TRUE,
        
#       # only patients with severe adverse events
#       subjectSubsetData = dataAll$AE,
#       subsetVar = "AETOXGR",
#       subsetValue = "SEVERE"
    )

3 Session information

R version 4.4.0 (2024-04-24)

Platform: x86_64-pc-linux-gnu

locale: C

attached base packages: stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: pander(v.0.6.5), clinUtils(v.0.2.0), patientProfilesVis(v.2.0.9) and knitr(v.1.47)

loaded via a namespace (and not attached): gtable(v.0.3.5), jsonlite(v.1.8.8), compiler(v.4.4.0), Rcpp(v.1.0.12), stringr(v.1.5.1), parallel(v.4.4.0), gridExtra(v.2.3), jquerylib(v.0.1.4), scales(v.1.3.0), yaml(v.2.3.8), fastmap(v.1.2.0), ggplot2(v.3.5.1), R6(v.2.5.1), plyr(v.1.8.9), htmlwidgets(v.1.6.4), forcats(v.1.0.0), tibble(v.3.2.1), munsell(v.0.5.1), bslib(v.0.7.0), pillar(v.1.9.0), rlang(v.1.1.4), utf8(v.1.2.4), DT(v.0.33), stringi(v.1.8.4), cachem(v.1.1.0), xfun(v.0.44), sass(v.0.4.9), viridisLite(v.0.4.2), cli(v.3.6.2), magrittr(v.2.0.3), crosstalk(v.1.2.1), digest(v.0.6.35), grid(v.4.4.0), haven(v.2.5.4), hms(v.1.1.3), cowplot(v.1.1.3), lifecycle(v.1.0.4), vctrs(v.0.6.5), evaluate(v.0.24.0), glue(v.1.7.0), data.table(v.1.15.4), fansi(v.1.0.6), colorspace(v.2.1-0), reshape2(v.1.4.4), rmarkdown(v.2.27), tools(v.4.4.0), pkgconfig(v.2.0.3) and htmltools(v.0.5.8.1)

Template to create patient profiles for SDTM datasets

Study: X, Batch X

Laure Cougnaud

June 18, 2024