Creation of standard in-text tables with the `inTextSummaryTable` package"
In inTextSummaryTable: Creation of in-Text Summary Table

Introduction

    library(knitr)
    opts_chunk$set(
        echo = TRUE, results = 'asis', warning = FALSE, 
        # stop document execution if error (not the default)
        error = FALSE, message = FALSE, cache = FALSE,
        fig.width = 8, fig.height = 7,
        fig.path = "./figures_vignette/",
        fig.align = 'center')
    # instead of warn = 0 by default
    # include warnings when they occur in the document
    options(width = 170)

    library(clinUtils)
    library(tools)# toTitleCase
    library(plyr) # for ddply, rbind.fill
    library(pander) # for session info
    library(inTextSummaryTable)

Data format

The package is demonstrated with a subset of the ADaM datasets from the CDISC Pilot 01 dataset, available in the clinUtils package.

    # load example data
    library(clinUtils)

    # load example data
    data(dataADaMCDISCP01)

    dataAll <- dataADaMCDISCP01
    labelVars <- attr(dataAll, "labelVars")

Typical in-text table for the CSR are included in the following sections.

Please note that the table content e.g. variables, statistics of interest depends strongly on the study at hand and personal preferences.

Subject information

Subject disposition

    # data of interest
    dataDM <- dataAll$ADSL

    varDMFL <- grep("FL$", colnames(dataDM), value = TRUE)
    varDMFLLabel <- sub(" Flag$", "", labelVars[varDMFL])

    getSummaryStatisticsTable(
        data = dataDM,
        var = varDMFL, varFlag = varDMFL, varGeneralLab = "Analysis Set, N", 
        varLab = varDMFLLabel,
        stats = getStats("n (%)"),
        colVar = "TRT01P",
        labelVars = labelVars,
        colTotalInclude = TRUE, colTotalLab = "All subjects",
        varInclude0 = TRUE,
        title = toTitleCase("Table: subject disposition"),
        file = file.path("tables_CSR", "Table_subjectDisposition.docx")
    )

Demographics

    # data of interest
    dataDM <- subset(dataAll$ADSL, SAFFL == "Y")

    # variables of interest
    # Note: if available: ethnicity is included
    varsDM <- c(
        "SEX", "AGE", "AGEGR1",
        "RACE", "ETHNIC",
        "HEIGHTBL", "WEIGHTBL", 
        "BMIBL", "BMIBLGR1"
    )

    # Sort variables according to corresponding numeric variable
    dataDM$AGEGR1 <- with(dataDM, reorder(AGEGR1, AGEGR1N))
    dataDM$RACE <- with(dataDM, reorder(RACE, RACEN))
    dataDM$TRT01P <- with(dataDM, reorder(TRT01P, TRT01PN))

    ## Define set of statistics of interest:
    statsDM <- getStatsData(
        data = dataDM, var = varsDM,
        # different for continuous and categorical variable
        type = c(cont = "median (range)", cat = "n (%)"),
        # for categorical variable, statistic name (here: 'n (%)')
        # should not be included in the table
        args = list(cat = list(includeName = FALSE))
    )

    ## create the table:

    getSummaryStatisticsTable(
        data = dataDM, 
        # variables to summarize
        var = varsDM, 
        varGeneralLab = "Parameter",
        # column
        colVar = "TRT01P", colTotalInclude = TRUE, colTotalLab = "All subjects",
        # statistics
        stats = statsDM,
        statsGeneralLab = "",
        labelVars = labelVars,
        # if only one category, should be included in separated row (e.g. RACE: White)
        rowAutoMerge = FALSE,
        rowInclude0 = FALSE, emptyValue = 0,
        title = toTitleCase("Table: Demographic Data (safety Analysis Set)"),
        file = file.path("tables_CSR", "Table_demographicData.docx")
    )

Baseline disease characteristics

Please note that the content of the table strongly depends on the study.

    # data of interest
    dataBDC <- subset(dataAll$ADSL, SAFFL == "Y")

    # create table
    getSummaryStatisticsTable(
        data = dataBDC,
        var = c("DURDIS", "EDUCLVL"), varGeneralLab = "Parameter", 
        colVar = "TRT01P", colTotalInclude = TRUE, colTotalLab = "All subjects",
        stats = getStats("median\n(range)"), statsGeneralLab = "",
        rowAutoMerge = FALSE,
        labelVars = labelVars,
        title = toTitleCase("Table: Baseline Disease Characteristics (safety analysis set)"),
        file = file.path("tables_CSR", "Table_BaselineCharacteristics.docx")
    )

Medical History and Concomitant Diseases

    dataCM <- subset(dataAll$ADCM, SAFFL == "Y")

    # sort variable according to corresponding numeric variables
    dataCM$TRTA <- with(dataCM, reorder(TRTA, TRTAN))

    # Terms should be in lower-case
    dataCM$CMDECOD <- simpleCap(tolower(dataCM$CMDECOD))
    dataCM$CMCLAS <- simpleCap(tolower(dataCM$CMCLAS))

    getSummaryStatisticsTable(
        data = dataCM,
        colVar = "TRTA", colTotalInclude = TRUE, colTotalLab = "All subjects",
        rowVar = c("CMCLAS", "CMDECOD"), 
        # include total across generic terms and across ATC4 classes
        rowVarTotalInclude = c("CMCLAS", "CMDECOD"), 
        rowTotalLab = "Any prior and concomitant medication",
        stats = getStats("n (%)"),
        # sort rows based on counts of subjects in the total column 
        rowOrder = "total",
        labelVars = labelVars,
        emptyValue = 0,
        title = toTitleCase(paste("Prior and concomitant therapies",
            "by medication class and generic term (safety analyis set)"
        )),
        file = file.path("tables_CSR", "Table_CM.docx")
    )

Efficacy Analyses

The example dataset has has two primary endpoints:

ADAS-Cog (11), a.k.a Alzheimer's Disease Assessment Scale - Cognitive Subscale a metric containing 11 items, available in the ADQSADAS dataset
CIBIC+ score a.k.a Video-referenced Clinician's Interview-based Impression of Change available in the ADQSCIBC dataset

    dataAdasCog11 <- subset(dataAll$ADQSADAS, PARAMCD == "ACTOT")
    dataCIBIC <- subset(dataAll$ADQSCIBC, PARAMCD == "CIBICVAL")

    dataEfficacy <- plyr::rbind.fill(dataAdasCog11, dataCIBIC)

    dataEfficacy$TRTP <- with(dataEfficacy, reorder(TRTP, TRTPN))
    dataEfficacy$AVISIT <- with(dataEfficacy, reorder(AVISIT, AVISITN))

    stats <- getStatsData(
        data = dataEfficacy, 
        var = c("AVAL", "CHG"), 
        type = c("n", "mean (se)", "median (range)")
    )

    getSummaryStatisticsTable(
        data = dataEfficacy,
        rowVar = "PARAM",
        colVar = c("TRTP", "AVISIT"),
        var = c("AVAL", "CHG"), 
        stats = stats,
        labelVars = labelVars,
        title = paste("Table: efficacy endpoints", 
            toTitleCase("actual value and changes from baseline per time point"             
        )),
        file = file.path("tables_CSR", "Table_efficacy.docx")
    )

Safety Analyses

Adverse Events

Treatment-emergent summary table

    ## data of interest: safety analysis set and treatment-emergent
    dataTEAE <- subset(dataAll$ADAE, SAFFL == "Y" & TRTEMFL == "Y")

    # order treatment and severity categories
    dataTEAE$TRTA <- with(dataTEAE, reorder(TRTA, TRTAN))

    ## data considered for the total
    dataTotalAE <- subset(dataAll$ADSL, SAFFL == "Y")
    dataTotalAE$TRTA <- with(dataTotalAE, reorder(TRT01A, TRT01AN))

    # TEAE with worst intensity
    # build worst-case scenario
    dataTEAE$AESEV <- factor(dataTEAE$AESEV, levels = c("MILD", "MODERATE", "SEVERE"))
    dataTEAE$AESEVN <- as.numeric(dataTEAE$AESEV)
    dataTEAE <- ddply(dataTEAE, c("USUBJID", "TRTA"), function(x)
        cbind.data.frame(x, 
            WORSTINT = with(x, ifelse(AESEVN == max(AESEVN), as.character(AESEV), NA_character_))
    ))
    dataTEAE$WORSTINT <- factor(dataTEAE$WORSTINT, levels = levels(dataTEAE$AESEV))

    ## specify labels for each variable:
    varsAE <- c("TRTEMFL", "AESER", "AESDTH", "AEREL")

    # create the table
    getSummaryStatisticsTable(
        data = dataTEAE,
        colVar = "TRTA",
        # define variables to compute statistics on
        var = c("TRTEMFL", "AESER", "WORSTINT", "AESDTH", "AEREL"), 
        varFlag = c("TRTEMFL", "AESER", "AESDTH"),
        varLab = c(TRTEMFL = "Treatment-Emergent", WORSTINT = "Worst-case severity:"),
        varGeneralLab = "Subjects with, n(%):",
        # force the inclusion of lines for variable without count:
        varInclude0 = TRUE,
        # include the total for the worst-case scenario
        varTotalInclude = "WORSTINT",
        # statistics:
        stats = getStats('n (%)'),
        emptyValue = "0",
        labelVars = labelVars,
        # dataset used for the total in the header column (and for percentage as default)
        dataTotal = dataTotalAE,
        # title/export
        title = toTitleCase("Table: Summary Table of Treatment-emergent Adverse Events (safety analysis set)"),
        file = file.path("tables_CSR", "Table_TEAE_summary.docx")
    )

Treatment-emergent incidence table

Events occuring in at least one subject

    dataTEAE <- subset(dataAll$ADAE, SAFFL == "Y" & TRTEMFL == "Y")

    # order treatment and severity categories
    dataTEAE$TRTA <- with(dataTEAE, reorder(TRTA, TRTAN))

    ## data considered for the total
    dataTotalAE <- subset(dataAll$ADSL, SAFFL == "Y")
    dataTotalAE$TRTA <- with(dataTotalAE, reorder(TRT01A, TRT01AN))

    getSummaryStatisticsTable(
        data = dataTEAE,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        ## total
        # data
        dataTotal = dataTotalAE,
        # row total
        rowVarTotalInclude = c("AESOC", "AEDECOD"), rowTotalLab = "Any TEAE",
        stats = getStats("n (%)"),
        labelVars = labelVars,
        rowVarLab = c('AESOC' = "TEAE by SOC and Preferred Term,\nn (%)"),
        # sort rows based on the total column:
        rowOrder = "total", 
        rowOrderTotalFilterFct = function(x) subset(x, TRTA == "Total"),
        title = paste("Table: Treatment-emergent Adverse Events by System Organ Class",
            "and Preferred Term (Safety Analysis Set)"
        ),
        file = file.path("tables_CSR", "Table_TEAE_SOCPT_atLeast1Subject.docx")
    )

Events occuring in at least 25% of all subjects

    getSummaryStatisticsTable(
        data = dataTEAE,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        ## total
        # data
        dataTotal = dataTotalAE, 
        # row total
        rowVarTotalInclude = c("AESOC", "AEDECOD"), rowTotalLab = "Any TEAE",
        stats = getStats("n (%)"),
        labelVars = labelVars,
        rowVarLab = c('AESOC' = "SOC and Preferred Term,\nn (%)"),
        # sort rows based on the total column:
        rowOrder = "total", 
        rowOrderTotalFilterFct = function(x) subset(x, TRTA == "Total"),
        title = paste("Table: Treatment-emergent Adverse Events by System Organ Class",
            "and Preferred Term reported in at least 25% of the subjects",
            "in any treatment group (Safety Analysis Set)"
        ),
        file = file.path("tables_CSR", "Table_TEAE_SOCPT_atLeast25PercentsSubject.docx"),
        # include only events occuring in at least 25% for at least one preferred term:
        filterFct = function(x)
            ddply(x, "AESOC", function(x){ # per AESOC to include the total
                ddply(x, "AEDECOD", function(y){
                    yTotal <- subset(y, grepl("Total", TRTA))
                    if(any(yTotal$statPercN >= 25)) y
                })
            })
    )

Treatment-emergent worst-case table

    dataTEAE <- subset(dataAll$ADAE, SAFFL == "Y" & TRTEMFL == "Y")

    # order treatment and severity categories
    dataTEAE$TRTA <- with(dataTEAE, reorder(TRTA, TRTAN))

    ## data considered for the total
    dataTotalAE <- subset(dataAll$ADSL, SAFFL == "Y")
    dataTotalAE$TRTA <- with(dataTotalAE, reorder(TRT01A, TRT01AN))

    # TEAE with worst intensity
    dataTEAE$AESEV <- factor(dataTEAE$AESEV, levels = c("MILD", "MODERATE", "SEVERE"))
    dataTEAE$AESEVN <- as.numeric(dataTEAE$AESEV)

    # extract worst-case scenario data (only one record if multiple with same severity)
    dataAEWC <- ddply(dataTEAE, c("AESOC", "AEDECOD", "USUBJID", "TRTA"), function(x){
        x[which.max(x$AESEVN), ]
    })
    # worst-case scenario in lower case
    dataAEWC$WORSTINT <- simpleCap(tolower(dataAEWC$AESEV))
    labelVars["WORSTINT"] <- "Worst-case scenario"

    ## datasets used for the total: 
    # for total: compute worst-case across SOC and across AE term
    # (otherwise patient counted in multiple categories if present different categories for different AEs)
    dataTotalRow <- list(
        # within SOC (across AEDECOD)
        'AEDECOD' = ddply(dataAEWC, c("AESOC", "USUBJID", "TRTA"), function(x){ 
            x[which.max(x$AESEVN), ]
        }),
        # across SOC
        'AESOC' = ddply(dataAEWC, c("USUBJID", "TRTA"), function(x){    
            x[which.max(x$AESEVN), ]
        })
    )

    getSummaryStatisticsTable(
        data = dataAEWC,
        ## row variables:
        rowVar = c("AESOC", "AEDECOD", "WORSTINT"), rowVarInSepCol = "WORSTINT",
        # include total across SOC and across AEDECOD
        rowVarTotalInclude = c("AESOC", "AEDECOD"), dataTotalRow = dataTotalRow, 
        rowVarTotalByVar = "WORSTINT", # count for each severity category for the total
        rowTotalLab = "Any TEAE", rowVarLab = c(AESOC = "Subjects with, n(%):", WORSTINT = "Worst-case scenario"),
        # sort per total in the total column
        rowOrder = "total", 
        ## column variables
        colVar = "TRTA", 
        stats = getStats("n (%)"),
        emptyValue = "0",
        labelVars = labelVars,
        dataTotal = dataTotalAE,
        title = toTitleCase(paste("Table: Treatment-emergent Adverse",
            "Events by system organ",
            "and preferred term by worst-case (safety Analysis Set)"
        )),
        file = file.path("tables_CSR", "Table_TEAE_Severity.docx")
    )

Laboratory safety

Table of laboratory abnormalities

    dataLBAbn <- subset(dataAll$ADLBC, SAFFL == "Y" & LBNRIND != "NORMAL")

    dataLBAbn$PARAM <- with(dataLBAbn, reorder(PARAM, PARAMN))
    dataLBAbn$TRTA <- with(dataLBAbn, reorder(TRTA, TRTAN))
    dataLBAbn$LBNRIND <- factor(dataLBAbn$LBNRIND, levels = c("LOW", "HIGH"))

    dataLBAbnTotal <- subset(dataAll$ADSL, SAFFL == "Y")
    dataLBAbnTotal$TRTA <- with(dataLBAbnTotal, reorder(TRT01A, TRT01AN))

    getSummaryStatisticsTable(
        data = dataLBAbn,
        rowVar = c("PARCAT1", "PARAM"), 
        rowVarTotalInclude = c("PARCAT1", "PARAM"),
        colVar = "TRTA", 
        var = "LBNRIND", 
        rowVarInSepCol = "variableGroup", varSubgroupLab = "Abnormality",
        rowVarLab = c('PARCAT1' = "Laboratory Parameter\nn (%)"),
        stats = getStats("n (%)"),
        labelVars = labelVars,
        rowOrder = c("PARCAT1" = "total", "PARAM" = "total", "variableGroup" = "auto"),
        dataTotal = dataLBAbnTotal, 
        title = toTitleCase(paste("Table: Treatment-emergent",
            "Worst-case Laboratory Abnormalities (safety analysis set)"
        )),
        emptyValue = "0",
        file = file.path("tables_CSR", "Table_Lab_Severity.docx")
    )

Electrocardiogram

Please note that there is no ECG dataset in the CDISC Pilot dataset used for the examples, so this table is not effectively created in the vignette.

Nevertheless, an example code is provided below to create a standard table of summary statistics for the ECG parameters.

    # data of interest
    paramsECG <- c("QT", "QTCF", "QRS", "PR", "RR", "EGHR")

    dataECG <- subset(dataAll$ADEG, SAFFL == "Y" & PARAMCD %in% paramsECG)
    dataECG$TRTA <- with(dataECG, reorder(TRTA, TRTAN))
    dataECG$PARAM <- with(dataECG, reorder(PARAM, PARAMN))

    # consider all non-missing post-baseline records
    dataECGPostBaseline <- subset(dataECG, 
        AVISIT %in% c("Screening", "Baseline", "Worst-case post-baseline")
    )

    # worst-case scenario:
    dataECGWC <- subset(dataECG, AVISIT == "Worst-case post-baseline")
    # treatment-emergent
    dataECGWC$TRTEMFL <- with(dataECGWC, ifelse(BASECAT1 != CHGCAT1, "Y", "N"))
    dataECGWCTE <- subset(dataECGWC, TRTEMFL == "Y")
    dataECGWC <- convertVarToFactor(dataECGWC, 
        var = c("AVALCAT1", "CHGCAT1"), 
        varNum = c("AVALCA1N", "CHGCAT1N")
    )

    # create the table
    getSummaryStatisticsTable(
        data = dataECGWC,
        # layout:
        colVar = "TRTA",
        rowVar = "PARAM", rowVarLab = c('PARAM' = "ECG Parameter"),
        # metrics to compute statistics on
        var = c("AVALCAT1", "CHGCAT1"),
        # in a separated column
        rowVarInSepCol = c("variable", "variableGroup"),
        # labels
        varGeneralLab = "Abnormality",
        varSubgroupLab = "Worst-Case Post-Baseline",
        stats = getStats("n (%)"),
        labelVars = labelVars,
        # total: all post-baseline
        dataTotal = dataECGPostBaseline, 
        emptyValue = "0",
        rowVarTotalPerc = "PARAM", # total per parameter
        # ensure that categories are below the type of abnormality
        rowAutoMerge = FALSE,
        # only retain abnormalities:
        filterFct = function(x){
            subset(x, !variableGroup %in% c("<= 450 msec", "<= 30 msec"))
        },
        title = toTitleCase(paste("Table: Treatment-emergent worst-case",
            "ECG abnormalities and change from baseline ECG abnormalities (safety analysis set)"
        )),
        file = file.path("tables_CSR", "Table_ECG.docx")
    )

Vital signs

Treatment-emergent vital signs abnormalities

    # analyis set and parameters of interest
    dataVS <- subset(dataAll$ADVS, 
        SAFFL == "Y" & ANL01FL == "Y" & VISIT != "BASELINE"
    )

    dataVS$PARAM <- with(dataVS, reorder(PARAM, PARAMN))
    dataVS$ANRIND <- with(dataVS, reorder(PARAM, PARAMN))
    dataVS$TRTA <- with(dataVS, reorder(TRTA, TRTAN))
    dataVS$SHIFT1 <- with(dataVS, factor(ifelse(SHIFT1 == "", NA_character_, SHIFT1)))

    getSummaryStatisticsTable(
        data = dataVS,
        rowVar = "PARAM", 
        rowVarInSepCol = "variableGroup", 
        rowVarInclude0 = TRUE,
        colVar = "TRTA", 
        var = "SHIFT1", varTotalInclude = TRUE,
        emptyValue = 0,
        stats = getStats("n (%)"),
        rowVarTotalPerc = "PARAM",
        labelVars = labelVars,
        title = toTitleCase(paste("Table: Treatment-emergent Worst-case",
            "Vital Sign Abnormalities (Safety Analysis Set)"
        )),
        file = file.path("tables_CSR", "Table_VitalSigns_Severity.docx")
    )

Pharmacokinetics analysis

Please note that this example pharmacodynamics dataset contains different subjects than the other datasets used in the vignette.

    paramcdPK <- c("AUCINFO", "CMAX", "TMAX")
    dataPK <- subset(dataAll$ADPP, PKFL == "Y" & PARAMCD %in% paramcdPK)

    dataPK$PARCAT1 <- with(dataPK, reorder(PARCAT1, PARCAT1N))
    dataPK$PARAMCD <- with(dataPK, reorder(PARAMCD, PARAMN))
    dataPK$TRTA <- with(dataPK, reorder(TRTA, TRTAN))
    dataPK$PARAMCD <- with(dataPK, reorder(PARAMCD, PARAMN))

    # build pretty labels
    labelsPK <- c(
        AUCINFO = "AUC_{Inf,obs}\n(h*ng/mL)",
        CMAX = "C_{max}\n(ng/mL)",
        TMAX = "t_{max}\n(h)"
    )
    dataPK$PARAM <- factor(dataPK$PARAMCD, 
        levels = levels(dataPK$PARAMCD), 
        labels = labelsPK[levels(dataPK$PARAMCD)]
    )

    statsPK <- dlply(dataPK, "PARAM", function(dataParam){
        getStatsData(
            data = dataParam,
            var = "AVAL",
            type = "median\n(range)",
            includeName = FALSE
        )[[1]]
    })

    getSummaryStatisticsTable(
        data = dataPK,
        rowVar = c("PARCAT1", "PARAM"), colVar = "TRTA",
        var = "AVAL",
#       rowVarLab = c('PARCAT1' = "PK parameters"),
        stats = statsPK, statsVarBy = "PARAM",
        emptyValue = "-",
        title = toTitleCase("Table: Summary of PK parameters (pharmacokinetics analysis set)"),
        file = file.path("tables_CSR", "Table_PK_Parameters.docx"),
        labelVars = labelVars
    )

Appendix

Session information

    pander(sessionInfo())

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inTextSummaryTable documentation built on Sept. 12, 2023, 5:06 p.m.

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inTextSummaryTable
Creation of in-Text Summary Table

Creation of standard in-text tables with the `inTextSummaryTable` package"
In inTextSummaryTable: Creation of in-Text Summary Table

Introduction

Data format

Subject information

Subject disposition

Demographics

Baseline disease characteristics

Medical History and Concomitant Diseases

Efficacy Analyses

Safety Analyses

Adverse Events

Treatment-emergent summary table

Treatment-emergent incidence table

Events occuring in at least one subject

Events occuring in at least 25% of all subjects

Treatment-emergent worst-case table

Laboratory safety

Table of laboratory abnormalities

Electrocardiogram

Vital signs

Treatment-emergent vital signs abnormalities

Pharmacokinetics analysis

Appendix

Session information

Try the inTextSummaryTable package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

inTextSummaryTable Creation of in-Text Summary Table

Creation of standard in-text tables with the `inTextSummaryTable` package" In inTextSummaryTable: Creation of in-Text Summary Table

Introduction

Data format

Subject information

Subject disposition

Demographics

Baseline disease characteristics

Medical History and Concomitant Diseases

Efficacy Analyses

Safety Analyses

Adverse Events

Treatment-emergent summary table

Treatment-emergent incidence table

Events occuring in at least one subject

Events occuring in at least 25% of all subjects

Treatment-emergent worst-case table

Laboratory safety

Table of laboratory abnormalities

Electrocardiogram

Vital signs

Treatment-emergent vital signs abnormalities

Pharmacokinetics analysis

Appendix

Session information

Try the inTextSummaryTable package in your browser

R Package Documentation

Browse R Packages

We want your feedback!

inTextSummaryTable
Creation of in-Text Summary Table

Creation of standard in-text tables with the `inTextSummaryTable` package"
In inTextSummaryTable: Creation of in-Text Summary Table