# Creation of standard in-text tables with the `inTextSummaryTable` package

#### Laure Cougnaud

#### February 22, 2022

# 1 Introduction

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

## 1.1 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.

# 2 Subject information

## 2.1 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")
        )

## 2.2 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")
        )

## 2.3 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")
        )

## 2.4 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")
        )

# 3 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")
        )

# 4 Safety Analyses

## 4.1 Adverse Events

### 4.1.1 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")
        )

### 4.1.2 Treatment-emergent incidence table

#### 4.1.2.1 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")
        )

#### 4.1.2.2 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
                    })
                })
        )

### 4.1.3 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")
        )

## 4.2 Laboratory safety

### 4.2.1 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")
        )

## 4.3 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")
        )

## 4.4 Vital signs

### 4.4.1 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")
        )

# 5 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
        )

# 6 Appendix

## 6.1 Session information

R version 4.1.2 (2021-11-01)

Platform: x86_64-pc-linux-gnu (64-bit)

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: tools, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: plyr(v.1.8.6), pander(v.0.6.4), clinUtils(v.0.1.1), inTextSummaryTable(v.3.1.1) and knitr(v.1.37)

loaded via a namespace (and not attached): tidyselect(v.1.1.1), xfun(v.0.29), reshape2(v.1.4.4), purrr(v.0.3.4), haven(v.2.4.3), colorspace(v.2.0-3), vctrs(v.0.3.8), generics(v.0.1.2), htmltools(v.0.5.2), viridisLite(v.0.4.0), yaml(v.2.3.5), base64enc(v.0.1-3), utf8(v.1.2.2), rlang(v.1.0.1), jquerylib(v.0.1.4), pillar(v.1.7.0), glue(v.1.6.1), gdtools(v.0.2.4), uuid(v.1.0-3), lifecycle(v.1.0.1), stringr(v.1.4.0), munsell(v.0.5.0), gtable(v.0.3.0), zip(v.2.2.0), htmlwidgets(v.1.5.4), evaluate(v.0.15), labeling(v.0.4.2), forcats(v.0.5.1), fastmap(v.1.1.0), crosstalk(v.1.2.0), fansi(v.1.0.2), highr(v.0.9), Rcpp(v.1.0.8), scales(v.1.1.1), DT(v.0.20), jsonlite(v.1.7.3), farver(v.2.1.0), systemfonts(v.1.0.4), ggplot2(v.3.3.5), hms(v.1.1.1), digest(v.0.6.29), stringi(v.1.7.6), dplyr(v.1.0.8), ggrepel(v.0.9.1), cowplot(v.1.1.1), grid(v.4.1.2), cli(v.3.2.0), magrittr(v.2.0.2), tibble(v.3.1.6), crayon(v.1.5.0), pkgconfig(v.2.0.3), ellipsis(v.0.3.2), data.table(v.1.14.2), xml2(v.1.3.3), rmarkdown(v.2.11), officer(v.0.4.1), flextable(v.0.6.10), R6(v.2.5.1) and compiler(v.4.1.2)