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:
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$", full.names = TRUE)
# dataAll <- loadDataADaMSDTM(files = pathFiles)
# labelVars <- attr(dataAll, "labelVars")For each domain of interest, the subject profiles are created. See the vignette:
mhPlots <- subjectProfileTextPlot(
data = dataAll$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)) cmPlots <- subjectProfileIntervalPlot(
data = dataAll$CM,
paramVar = c(
"CMTRT",
"CMDOSE", "CMDOSU", "CMROUTE",
"CMDOSFRQ"
),
timeStartVar = "CMSTDY",
timeEndVar = "CMENDY",
# optional:
paramGroupVar = "CMCLAS", # or CMINDC
colorVar = "CMCLAS", # or CMINDC
labelVars = labelVars,
title = "Concomitant medications",
# To zoom in axis scale in study time frame
# (to avoid scale is focused on negative pre-study time frame for CM)
timeTrans = getTimeTrans(type = "asinh-neg"),
alpha = 0.8,
timeAlign = FALSE
)## 171 record(s) with missing Study Day of Start of Medication and 208 record(s) with missing Study Day of End of Medication are imputed with minimal imputation. exPlots <- subjectProfileIntervalPlot(
data = dataAll$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)) 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$AE,
paramVar = "AEDECOD", # AETERM, depending on coding
timeStartVar = "AESTDY",
timeEndVar = "AEENDY",
# optional:
paramGroupVar = "AESOC",
colorVar = "AESEV",
title = "Adverse events",
timeAlign = FALSE,
alpha = 0.8,
labelVars = labelVars
)## 3 record(s) with missing Study Day of Start of Adverse Event and 19 record(s) with missing Study Day of End of Adverse Event are imputed with minimal imputation. dataLB <- dataAll$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)) # If available, reference range indicator
# could be displayed via the color/shape variable
vsPlots <- subjectProfileLinePlot(
data = dataAll$VS,
paramValueVar = "VSSTRESN",
paramNameVar = "VSTEST",
timeVar = "VSDY",
# optional
# paramGroupVar = "VSCAT", # if available
colorVar = "VSPOS", # if available
shapeSize = 2,
title = "Vital signs",
alpha = 0.8,
labelVars = labelVars
)## Empty records in the: 'VSPOS' variable are converted to NA.Note: no ECG data is available for the example dataset, so this domain is not considered for the example.
# If available, reference range indicator
# could be displayed via the color/shape variable
egPlots <- subjectProfileLinePlot(
data = dataAll$EG,
paramValueVar = "EGSTRESN",
paramNameVar = "EGTEST",
timeVar = "EGDY",
# optional
title = "Electrocardiogram",
alpha = 0.8,
labelVars = labelVars
)
patientProfilesPlots <- c(patientProfilesPlots, list(EG = egPlots))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"
)R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=C, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=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), knitr(v.1.48) and rmarkdown(v.2.28)
loaded via a namespace (and not attached): gtable(v.0.3.5), jsonlite(v.1.8.9), compiler(v.4.4.1), Rcpp(v.1.0.13), stringr(v.1.5.1), parallel(v.4.4.1), gridExtra(v.2.3), jquerylib(v.0.1.4), scales(v.1.3.0), yaml(v.2.3.10), 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), maketools(v.1.3.1), munsell(v.0.5.1), bslib(v.0.8.0), 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.0.48), sass(v.0.4.9), sys(v.3.4.3), viridisLite(v.0.4.2), cli(v.3.6.3), magrittr(v.2.0.3), crosstalk(v.1.2.1), digest(v.0.6.37), grid(v.4.4.1), 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.1.0.1), glue(v.1.8.0), data.table(v.1.16.2), buildtools(v.1.0.0), fansi(v.1.0.6), colorspace(v.2.1-1), reshape2(v.1.4.4), tools(v.4.4.1), pkgconfig(v.2.0.3) and htmltools(v.0.5.8.1)