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")
    )

Table: Subject Disposition

Analysis Set, N

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

All subjects
(N=7)

Safety Population

2 (100)

3 (100)

2 (100)

7 (100)

Intent-to-Treat Population

2 (100)

3 (100)

2 (100)

7 (100)

Efficacy Population

1 (50.0)

3 (100)

2 (100)

6 (85.7)

Completers of Week 8 Population

1 (50.0)

3 (100)

2 (100)

6 (85.7)

Completers of Week 16 Population

1 (50.0)

1 (33.3)

1 (50.0)

3 (42.9)

Completers of Week 24 Population

1 (50.0)

1 (33.3)

1 (50.0)

3 (42.9)

Did the Subject Discontinue the Study?

2 (100)

2 (66.7)

1 (50.0)

5 (71.4)

Discontinued due to AE?

0

1 (33.3)

0

1 (14.3)

Subject Died?

2 (100)

0

1 (50.0)

3 (42.9)

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")
    )

Table: Demographic Data (Safety Analysis Set)

Parameter

Placebo
(N=2)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

All subjects
(N=7)

Variable group

Sex

F

1 (50.0)

2 (100)

2 (66.7)

5 (71.4)

M

1 (50.0)

0

1 (33.3)

2 (28.6)

Age

Median (range)

82.0 (75,89)

78.0 (76,80)

69.0 (57,74)

75.0 (57,89)

Pooled Age Group 1

<65

0

0

1 (33.3)

1 (14.3)

65-80

1 (50.0)

2 (100)

2 (66.7)

5 (71.4)

>80

1 (50.0)

0

0

1 (14.3)

Race

WHITE

2 (100)

2 (100)

2 (66.7)

6 (85.7)

BLACK OR AFRICAN AMERICAN

0

0

1 (33.3)

1 (14.3)

Ethnicity

NOT HISPANIC OR LATINO

2 (100)

2 (100)

3 (100)

7 (100)

Baseline Height (cm)

Median (range)

167.65 (157.5,177.8)

155.55 (151.1,160.0)

158.80 (154.9,175.3)

158.80 (151.1,177.8)

Baseline Weight (kg)

Median (range)

59.65 (47.2,72.1)

54.45 (45.4,63.5)

66.70 (51.7,87.1)

63.50 (45.4,87.1)

Baseline BMI (kg/m^2)

Median (range)

20.90 (19.0,22.8)

22.75 (17.7,27.8)

27.80 (20.5,28.3)

22.80 (17.7,28.3)

Pooled Baseline BMI Group 1

25-<30

0

1 (50.0)

2 (66.7)

3 (42.9)

<25

2 (100)

1 (50.0)

1 (33.3)

4 (57.1)

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")
    )

Table: Baseline Disease Characteristics (Safety Analysis Set)

Parameter

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

All subjects
(N=7)

Duration of Disease (Months)

20.65
(17.6,23.7)

31.4
(2.2,32.1)

35.6
(31.4,39.8)

31.4
(2.2,39.8)

Years of Education

13
(12,14)

15
(10,16)

12
(12,12)

12
(10,16)

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")
    )

Prior and Concomitant Therapies by Medication Class and Generic Term (Safety Analyis Set)

Medication Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=1)

All subjects
(N=3)

Standardized Medication Name

Any prior and concomitant medication

2 (100)

1 (100)

3 (100)

Systemic hormonal preparations, excl.

2 (100)

1 (100)

3 (100)

Hydrocortisone

2 (100)

1 (100)

3 (100)

Uncoded

2 (100)

1 (100)

3 (100)

Uncoded

2 (100)

1 (100)

3 (100)

Respiratory system

1 (50.0)

0

1 (33.3)

Salbutamol sulfate

1 (50.0)

0

1 (33.3)

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")
    )

Table: efficacy endpoints Actual Value and Changes from Baseline per Time Point

Parameter

Placebo

Xanomeline Low Dose

Xanomeline High Dose

Variable

Statistic

Baseline
(N=2)

Week 8
(N=2)

Week 16
(N=2)

Week 24
(N=2)

Baseline
(N=2)

Week 8
(N=2)

Week 16
(N=2)

Week 24
(N=2)

Baseline
(N=3)

Week 8
(N=3)

Week 16
(N=3)

Week 24
(N=3)

Adas-Cog(11) Subscore

Analysis Value

n

2

2

2

2

2

2

2

2

3

3

3

3

Mean (SE)

14.0 (6.00)

16.5 (3.50)

17.5 (2.50)

17.5 (2.50)

32.0 (23.00)

37.0 (25.00)

38.5 (23.50)

37.0 (25.00)

15.7 (6.36)

16.7 (6.36)

15.3 (5.21)

17.7 (5.36)

Median (range)

14.0 (8,20)

16.5 (13,20)

17.5 (15,20)

17.5 (15,20)

32.0 (9,55)

37.0 (12,62)

38.5 (15,62)

37.0 (12,62)

15.0 (5,27)

16.0 (6,28)

16.0 (6,24)

22.0 (7,24)

Change from Baseline

n

-

2

2

2

-

2

2

2

-

3

3

3

Mean (SE)

-

2.5 (2.50)

3.5 (3.50)

3.5 (3.50)

-

5.0 (2.00)

6.5 (0.50)

5.0 (2.00)

-

1.0 (0.00)

-0.3 (1.33)

2.0 (2.89)

Median (range)

-

2.5 (0,5)

3.5 (0,7)

3.5 (0,7)

-

5.0 (3,7)

6.5 (6,7)

5.0 (3,7)

-

1.0 (1,1)

1.0 (-3,1)

2.0 (-3,7)

CIBIC Score

Analysis Value

n

-

1

1

1

-

2

2

2

-

2

3

3

Mean (SE)

-

6.0 ( NA)

5.0 ( NA)

5.0 ( NA)

-

5.0 (0.00)

4.5 (0.50)

4.5 (0.50)

-

4.5 (0.50)

4.0 (0.00)

4.3 (0.33)

Median (range)

-

6.0 (6,6)

5.0 (5,5)

5.0 (5,5)

-

5.0 (5,5)

4.5 (4,5)

4.5 (4,5)

-

4.5 (4,5)

4.0 (4,4)

4.0 (4,5)

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")
    )

Table: Summary Table of Treatment-Emergent Adverse Events (Safety Analysis Set)

Subjects with, n(%):

Placebo
(N=2)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Variable group

Treatment-Emergent

2 (100)

2 (100)

3 (100)

Serious Event

0

0

1 (33.3)

Worst-case severity:

2 (100)

2 (100)

3 (100)

MILD

0

0

0

MODERATE

0

1 (50.0)

1 (33.3)

SEVERE

2 (100)

1 (50.0)

2 (66.7)

Results in Death

2 (100)

1 (50.0)

0

Causality

NONE

1 (50.0)

2 (100)

3 (100)

POSSIBLE

1 (50.0)

1 (50.0)

2 (66.7)

PROBABLE

0

1 (50.0)

3 (100)

REMOTE

0

0

1 (33.3)

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")
    )

Table: Treatment-emergent Adverse Events by System Organ Class and Preferred Term (Safety Analysis Set)

TEAE by SOC and Preferred Term,
n (%)

Placebo
(N=2)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

Any TEAE

2 (100)

2 (100)

3 (100)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

0

2 (100)

3 (100)

APPLICATION SITE PRURITUS

0

2 (100)

2 (66.7)

APPLICATION SITE ERYTHEMA

0

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

0

1 (50.0)

1 (33.3)

APPLICATION SITE DERMATITIS

0

0

1 (33.3)

FATIGUE

0

0

1 (33.3)

SECRETION DISCHARGE

0

1 (50.0)

0

SUDDEN DEATH

0

1 (50.0)

0

MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS

0

2 (100)

2 (66.7)

BACK PAIN

0

0

1 (33.3)

FLANK PAIN

0

0

1 (33.3)

MUSCULAR WEAKNESS

0

1 (50.0)

0

SHOULDER PAIN

0

1 (50.0)

0

PSYCHIATRIC DISORDERS

1 (50.0)

1 (50.0)

1 (33.3)

COMPLETED SUICIDE

1 (50.0)

0

0

CONFUSIONAL STATE

0

1 (50.0)

0

HALLUCINATION, VISUAL

0

0

1 (33.3)

GASTROINTESTINAL DISORDERS

0

0

2 (66.7)

NAUSEA

0

0

2 (66.7)

INFECTIONS AND INFESTATIONS

0

1 (50.0)

1 (33.3)

LOWER RESPIRATORY TRACT INFECTION

0

0

1 (33.3)

PNEUMONIA

0

1 (50.0)

0

NERVOUS SYSTEM DISORDERS

0

0

2 (66.7)

AMNESIA

0

0

1 (33.3)

LETHARGY

0

0

1 (33.3)

PARTIAL SEIZURES WITH SECONDARY GENERALISATION

0

0

1 (33.3)

RENAL AND URINARY DISORDERS

0

1 (50.0)

1 (33.3)

CALCULUS URETHRAL

0

0

1 (33.3)

INCONTINENCE

0

1 (50.0)

0

RESPIRATORY, THORACIC AND MEDIASTINAL DISORDERS

0

1 (50.0)

1 (33.3)

DYSPNOEA

0

1 (50.0)

0

EPISTAXIS

0

0

1 (33.3)

SKIN AND SUBCUTANEOUS TISSUE DISORDERS

0

1 (50.0)

1 (33.3)

ACTINIC KERATOSIS

0

0

1 (33.3)

ERYTHEMA

0

1 (50.0)

0

CARDIAC DISORDERS

1 (50.0)

0

0

MYOCARDIAL INFARCTION

1 (50.0)

0

0

INJURY, POISONING AND PROCEDURAL COMPLICATIONS

0

1 (50.0)

0

JOINT DISLOCATION

0

1 (50.0)

0

SKIN LACERATION

0

1 (50.0)

0

INVESTIGATIONS

0

1 (50.0)

0

NASAL MUCOSA BIOPSY

0

1 (50.0)

0

METABOLISM AND NUTRITION DISORDERS

0

0

1 (33.3)

DECREASED APPETITE

0

0

1 (33.3)

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
                })
            })
    )

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)

SOC and Preferred Term,
n (%)