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This vignette focuses on how to create in-text tables with the inTextSummaryTable package.

In this vignette we assume you have ready the data.frame(s) to create the tables. If you have doubts on the data format, please look the introductory vignette at the section “data format”.

We will use the example data available in the clinUtils package. Let’s load the packages and the data, and get started!

    library(inTextSummaryTable)
    library(pander)
    library(tools) # toTitleCase
    library(clinUtils)

    # load example data
    data(dataADaMCDISCP01)
    
    dataAll <- dataADaMCDISCP01
    labelVars <- attr(dataAll, "labelVars")

The getSummaryStatisticsTable creates an in-text table of summary statistics for variable(s) of interest.

The Demographic data (ADSL dataset) is used as example for the summary statistics table.

    dataSL <- dataAll$ADSL

1 Variable(s) to summarize

Variable(s) to summarize in the table are specified via the var parameter.

Different set of statistics are reported depending on the type of variable: Categorical variable or Continuous variable.

See the documentation in section Base statistics for more details on the statistics included by default for each type, via:

? `inTextSummaryTable-stats` 

1.1 Categorical variable

For a discrete/categorical variable, the in-text table can display the counts/percentages of the number of subjects or records for each category of the variable.

1.1.1 Counts of the entire dataset

If no variable is specified (via the var parameter), the counts are displayed for the entire dataset.

    getSummaryStatisticsTable(data = dataSL)

Statistic

StatisticValue
(N=7)

statN

7

statm

7

statPercTotalN

7

statPercN

100

Please note that this is equivalent of setting (var = 'all').

1.1.2 Counts of categories

If a variable is specified (via the var parameter), the counts are displayed for each category.

    getSummaryStatisticsTable(data = dataSL, var = "SEX")

Variable group

StatisticValue
(N=7)

Statistic

F

statN

5

statm

5

statPercTotalN

7

statPercN

71.43

M

statN

2

statm

2

statPercTotalN

7

statPercN

28.57

1.1.3 Sort categories

The categories of the variable are sorted alphabetically by default. To sort the categories in a specific order, the variable should be formatted as factor, whose ordered categories are included in its levels.

    # specify manually the order of the categories
    dataSL$SEX <- factor(dataSL$SEX, levels = c("M", "F"))
    getSummaryStatisticsTable(data = dataSL, var = "SEX")

Variable group

StatisticValue
(N=7)

Statistic

M

statN

2

statm

2

statPercTotalN

7

statPercN

28.57

F

statN

5

statm

5

statPercTotalN

7

statPercN

71.43

    # order categories based on a numeric variable
    dataSL$SEXN <- ifelse(dataSL$SEX == "M", 2, 1)
    dataSL$SEX <- reorder(dataSL$SEX, dataSL$SEXN)
    getSummaryStatisticsTable(data = dataSL, var = "SEX")

Variable group

StatisticValue
(N=7)

Statistic

F

statN

5

statm

5

statPercTotalN

7

statPercN

71.43

M

statN

2

statm

2

statPercTotalN

7

statPercN

28.57

1.1.4 Inclusion of categories not available in the data

By default, the table only includes the categories present in the input data, to ensure a compact table for CSR export.

    dataSLExample <- dataSL
    
    # 'SEX' formatted as character with only male
    dataSLExample$SEX <- "M" # only male
    getSummaryStatisticsTable(data = dataSLExample, var = "SEX")

Variable group

StatisticValue
(N=7)

Statistic

M

statN

7

statm

7

statPercTotalN

7

statPercN

100

If extra categories should be represented in the table, the categorical variable should be formatted as a factor, whose levels contain all categories to be displayed in the table.

Furthermore, the parameter: varInclude0 should be set to TRUE or to the specific variable (in case multiple variables are specified) to indicate that categories with 0 counts should be included.

    # 'SEX' formatted as factor, to include also female in the table
    # (even if not available in the data)
    dataSLExample$SEX <- factor("M", levels = c("F", "M"))
    getSummaryStatisticsTable(data = dataSLExample, var = "SEX", varInclude0 = TRUE)

Variable group

StatisticValue
(N=7)

Statistic

F

statN

0

statm

0

statPercTotalN

7

statPercN

0

M

statN

7

statm

7

statPercTotalN

7

statPercN

100

    # or:
    getSummaryStatisticsTable(data = dataSLExample, var = "SEX", varInclude0 = "SEX")

Variable group

StatisticValue
(N=7)

Statistic

F

statN

0

statm

0

statPercTotalN

7

statPercN

0

M

statN

7

statm

7

statPercTotalN

7

statPercN

100

1.1.5 Count table for ‘flag’-variables

A specific type of categorical variable is a ‘flag variable’, which indicates if a record fulfills a specific criteria.

Such variable is typically formatted in the data as:

  • ‘Y’ if the criteria is met for the specific record
  • ‘N’ if the criteria is not fulfilled for the specific record
  • ’’ if the criteria is missing for this record

The name of such variable typically ends with ‘FL’ in a CDISC-compliant ADaM or SDTM dataset.

For example, the subject-level dataset contains the following flag variables:

    labelVars[grep("FL$", colnames(dataSL), value = TRUE)]
##                                    SAFFL                                    ITTFL                                    EFFFL                                  COMP8FL 
##                 "Safety Population Flag"        "Intent-to-Treat Population Flag"               "Efficacy Population Flag"   "Completers of Week 8 Population Flag" 
##                                 COMP16FL                                 COMP24FL                                 DISCONFL                                  DSRAEFL 
##  "Completers of Week 16 Population Flag"  "Completers of Week 24 Population Flag" "Did the Subject Discontinue the Study?"                "Discontinued due to AE?" 
##                                    DTHFL 
##                          "Subject Died?"
    # has the subject discontinued from the study?
    dataSL$DISCONFL
## [1] ""  ""  "Y" "Y" "Y" "Y" "Y"

If this variable is specified in var, the counts for each category is reported:

    getSummaryStatisticsTable(
        data = dataSL,
        var = "SAFFL"
    )

Variable group

StatisticValue
(N=7)

Statistic

Y

statN

7

statm

7

statPercTotalN

7

statPercN

100

However, the interest is often to only reports the counts for the records fulfilling the criteria (records with ‘Y’). This is the case if the variable is specified via the varFlag parameter too.

    getSummaryStatisticsTable(
        data = dataSL,
        var = "SAFFL",
        varFlag = "SAFFL"
    )

Statistic

StatisticValue
(N=7)

statN

7

statm

7

statPercTotalN

7

statPercN

100

1.1.6 Inclusion of total across categories

To include the total counts across categories, the varTotalInclude parameter should be set to TRUE (or to the specific variable).

    getSummaryStatisticsTable(
        data = dataSL, 
        var = "SEX", 
        varTotalInclude = TRUE
    )

Variable group

StatisticValue
(N=7)

Statistic

Total

statN

7

statm

7

statPercTotalN

7

statPercN

100

F

statN

5

statm

5

statPercTotalN

7

statPercN

71.43

M

statN

2

statm

2

statPercTotalN

7

statPercN

28.57

1.2 Continuous variable

For a continuous variable, the in-text table displays standard distribution statistics of the variable.

Please note that missing records (NA) for the variable are filtered, so the count statistics (number of subjects, records, percentage) are based only on the non missing records.

For a continuous variable, the presence of different values for the same subject (and across row/column variables) are checked and an appropriate error message is returned if multiple different values are available.

    getSummaryStatisticsTable(data = dataSL, var = "AGE")

Statistic

StatisticValue
(N=7)

statN

7

statm

7

statMean

74.29

statSD

9.827

statSE

3.714

statMedian

75

statMin

57

statMax

89

statPercTotalN

7

statPercN

100

1.3 Continuous and categorical variables in the table

The table can contain a mix of categorical and continuous variables.

    getSummaryStatisticsTable(
        data = dataSL, 
        var = c("AGE", "SEX")
    )

Variable

StatisticValue
(N=7)

Variable group

Statistic

AGE

statN

7

statm

7

statMean

74.29

statSD

9.827

statSE

3.714

statMedian

75

statMin

57

statMax

89

statPercTotalN

7

statPercN

100

SEX

F

statN

5

statm

5

statPercTotalN

7

statPercN

71.43

M

statN

2

statm

2

statPercTotalN

7

statPercN

28.57

2 Statistics of interest

Statistics of interest and their format are specified via the stats parameter.

If an unique statistic expression is specified, the ‘Statistic’ column doesn’t appear in the table.
In case multiple statistics are specified, these are included as separated row.

2.1 Standard statistic set

A standard set of statistics is specified via specific tags to be passed to the stats function.

The list of available statistics is mentioned in the section ‘Formatted statistics’ in:

    ? `inTextSummaryTable-stats` 

Please see below examples of commonly used statistics.

2.1.1 Categorical table

    # count: n, '%' and m
    getSummaryStatisticsTable(
        data = dataSL,
        var = "SEX",
        stats = "count"
    )

Variable group

StatisticValue
(N=7)

Statistic

F

n

5

%

71.4

m

5

M

n

2

%

28.6

m

2

    # n (%)
    getSummaryStatisticsTable(
        data = dataSL,
        var = "SEX",
        stats = "n (%)"
    )

Variable group

n (%)
(N=7)

F

5 (71.4)

M

2 (28.6)

    # n/N (%)
    getSummaryStatisticsTable(
        data = dataSL,
        var = "SEX",
        stats = "n/N (%)"
    )

Variable group

n/N (%)
(N=7)

F

5/7 (71.4)

M

2/7 (28.6)

2.1.2 Continuous variable

    ## continuous variable
    
    # all summary stats
    getSummaryStatisticsTable(
        data = dataSL,
        var = "AGE",
        stats = "summary"
    )

Statistic

StatisticValue
(N=7)

n

7

Mean

74.3

SD

9.8

SE

3.71

Median

75.0

Min

57

Max

89

%

100

m

7

    # median (range)
    getSummaryStatisticsTable(
        data = dataSL,
        var = "AGE",
        stats = "median (range)"
    )

Median (range)
(N=7)

75.0 (57,89)

    # median and (range) in a different line:
    getSummaryStatisticsTable(
        data = dataSL,
        var = "AGE",
        stats = "median\n(range)"
    )

Median
(range)
(N=7)

75.0
(57,89)

    # mean (se)
    getSummaryStatisticsTable(
        data = dataSL,
        var = "AGE",
        stats = "mean (se)"
    )

Mean (SE)
(N=7)

74.3 (3.71)

    # mean (sd)
    getSummaryStatisticsTable(
        data = dataSL,
        var = "AGE",
        stats = "mean (sd)"
    )

Mean (SD)
(N=7)

74.3 (9.8)

2.2 Custom statistics formatting (Advanced)

To change the formatting of the statistics, the stats parameter should contain a language object (e.g. expression or call) of the default base set of statistics.

See the documentation in section ‘Base statistics’ for more details on the base statistics included by default, via:

? `inTextSummaryTable-stats` 

For example, the following count table is restricted to the number of subjects per categories:

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("RACE", "SEX"),
        stats = list(N = expression(statN))
    )

Variable

N
(N=7)

Variable group

RACE

BLACK OR AFRICAN AMERICAN

1

WHITE

6

SEX

F

5

M

2

The summary statistics table is restricted to the median and range:

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL", "WEIGHTBL", "BMIBL"),
        varGeneralLab = "Parameter", statsGeneralLab = "",
        colVar = "TRT01P",
        stats = list(
            `median` = expression(statMedian),
            `(min, max)` = expression(paste0("(", statMin, ",", statMax, ")"))
        )
    )

Parameter

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

AGE

median

82

69

78

(min, max)

(75,89)

(57,74)

(76,80)

HEIGHTBL

median

167.65

158.8

155.55

(min, max)

(157.5,177.8)

(154.9,175.3)

(151.1,160)

WEIGHTBL

median

59.65

66.7

54.45

(min, max)

(47.2,72.1)

(51.7,87.1)

(45.4,63.5)

BMIBL

median

20.9

27.8

22.75

(min, max)

(19,22.8)

(20.5,28.3)

(17.7,27.8)

Note that the ‘Standard statistics set’ is formatted internally via the getStatsData (and getStats) functions, which creates consistently a list of language objects.

    # this count table:
    getSummaryStatisticsTable(
        data = dataSL,
        var = "SEX",
        stats = "count"
    )

Variable group

StatisticValue
(N=7)

Statistic

F

n

5

%

71.4

m

5

M

n

2

%

28.6

m

2

    # ... is equivalent to:
    getSummaryStatisticsTable(
        data = dataSL,
        var = "SEX",
        stats = getStats(type = "count")
    )

Variable group

StatisticValue
(N=7)

Statistic

F

n

5

%

71.4

m

5

M

n

2

%

28.6

m

2

    # this summary table...
    getSummaryStatisticsTable(
        data = dataSL,
        var = "AGE",
        stats = "mean (se)"
    )

Mean (SE)
(N=7)

74.3 (3.71)

    # ... is equivalent to:
    getSummaryStatisticsTable(
        data = dataSL,
        var = "AGE",
        stats = getStatsData(type = "mean (se)", var = "AGE", data = dataSL)[["AGE"]]
    )

Mean (SE)
(N=7)

74.3 (3.71)

2.3 Statistics by variable/group

The statistics can also be provided for each variable separately, if stats is named by variable:

    getSummaryStatisticsTable(
        data = dataSL, 
        var = c("AGE", "RACE"),
        stats = list(
            AGE = getStats("median (range)"),
            RACE = getStats("n (%)")
        )
    )

Variable

StatisticValue
(N=7)

Variable group

Statistic

AGE Median (range)

75 (57,89)

RACE

BLACK OR AFRICAN AMERICAN n (%)

1 (14.3)

WHITE n (%)

6 (85.7)

2.4 Extra statistics

Extra statistics (not available in the default set of statistics) should be specified via the statsExtra parameter.

A set of extra utility functions to compute common extra statistics are also available in the package:

  • coefficient of variation with the cv function
  • geometric mean with the geomMean function
  • geometric standard deviation with the geomSD function
  • geometric coefficient of variation with the geomCV function
    getSummaryStatisticsTable(
        data = dataSL,
        var = "HEIGHTBL",
        # specify extra stats to compute
        statsExtra = list(
            statCV = cv,
            statGeomMean = geomMean,
            statGeomSD = geomSD,
            statsGeomCV = geomCV
        )
    )

Statistic

StatisticValue
(N=7)

statN

7

statm

7

statMean

162.2

statSD

10.25

statSE

3.873

statMedian

158.8

statMin

151.1

statMax

177.8

statCV

6.317

statGeomMean

161.9

statGeomSD

1.064

statsGeomCV

6.21

statPercTotalN

7

statPercN

100

Full customized statistics can also be provided. For example, if you would like to specify your own formula for the coefficient of variation:

    # include the coefficient of variation via the 'statsExtra' parameter
    getSummaryStatisticsTable(
        data = dataSL,
        var = "HEIGHTBL",
        statsExtra = list(statCVPerc = function(x) sd(x)/mean(x)*100)
    )

Statistic

StatisticValue
(N=7)

statN

7

statm

7

statMean

162.2

statSD

10.25

statSE

3.873

statMedian

158.8

statMin

151.1

statMax

177.8

statCVPerc

6.317

statPercTotalN

7

statPercN

100

These statistics are then available for customization via the stats parameter.

    # format the statistics with the 'stats' parameter
    getSummaryStatisticsTable(
        data = dataSL,
        var = "HEIGHTBL",
        statsExtra = list(statCVPerc = function(x) sd(x)/mean(x)*100),
        stats = list(Mean = expression(statMean), 'CV%' = expression(statCVPerc))
    )

Statistic

StatisticValue
(N=7)

Mean

162.2

CV%

6.317

2.5 Rounding strategy

Please note that all statistics are rounded by default in the package based on the ‘rounding up’ strategy for rounding off a 5, which differs from the default rounding strategy in R (round function).

This was a deliberate choice to reproduce summarized statistics created with the SAS software.

Please find more explanations in the documentation of the ? roundHalfUp and ? roundHalfUpTextFormat functions.

2.6 Number of decimals

The detailed rules for the number of decimals for the statistics are described in the section Statistics formatting in:

    ? `inTextSummaryTable-stats` 

To specify fixed amounts of digits for the statistics to be displayed in the table, the statistics are formatted in the stats parameter.

2.6.1 Default number of decimals

2.6.1.1 Categorical variable

The percentages are formatted by default as specified in the table below.

Standard Layout for Frequency Tabulations of Categorical Variables<br>

Standard Layout for Frequency Tabulations of Categorical Variables

By default, the counts for a categorical variables are formatted as specified above:

  • the number of subjects is displayed with 0 digits (nDecN is set to 0)
  • the frequency percentage is implemented in the formatPercentage function
    # Internal rule for the number of decimals for the percentage
    formatPercentage(c(NA, 0, 100, 99.95, 0.012, 34.768))
## [1] "-"     "0"     "100"   ">99.9" "<0.1"  "34.8"
    # Used by default in the 'getStats' function
    getStats(type = "count")
## $n
## roundHalfUpTextFormat(statN, 0)
## 
## $`%`
## (function (x, nDec = 1) 
## {
##     xRF <- ifelse(is.na(x), "-", ifelse(x == 0, "0", ifelse(x == 
##         100, "100", ifelse(x < 0.1, "<0.1", ifelse(x > 99.9, 
##         ">99.9", roundHalfUpTextFormat(x, digits = nDec))))))
##     return(xRF)
## })(statPercN)
## 
## $m
## roundHalfUpTextFormat(statm, 0)

2.6.1.2 Continuous variable

The number of decimals for statistics based on a continuous variable is by default as specified in the tables below.

Standard Layout for Descriptive Statistics of Continuous Variables<br>

Standard Layout for Descriptive Statistics of Continuous Variables

In the package: ‘Very small values’ are considered values below 1.

When specifying the default set of available statistics with the getStats function, and only if the variable is specified (x parameter), the number of decimals for a continuous variable is determined by:

  1. Extracting the number of decimals for individual values based on:
    • pre-defined rules based on the number of decimals of the individual values (getNDecimalsRule function)
    • the number of decimals available in the input data via the getNDecimalsData function
    • taking the minimum of these two criterias (getNDecimals function), such as the number of decimals according the rule won’t be higher that the actual number of decimals available in the data
  2. Taking the maximum number of decimals across all individual values via the getMaxNDecimals function, which is used as ‘base’ number of decimals considered for the summary statistics
  3. The actual number of decimals for each statistic is extracted by adding to the ‘base’ number of decimals:
    • 0 extra decimal for the minimum, maximum
    • 1 extra decimal for the mean, median, sd
    • 2 extra decimals for SE

Please note that if a different framework than implemented in steps 1 and 2 should be used for the extraction of the number of decimals for a specific variable, the number of decimals of interest can be fixed via the nDecCont parameter.

    # Duration of Disease (Months)
    print(dataSL$DURDIS)
## [1] 32.1 39.8 31.4 17.6 23.7  2.2 31.4
    ## Extract the number of decimals for each value:
    
    # based on pre-defined rule, this metric should be displayed with 1 decimal:
    getNDecimalsRule(x = dataSL$DURDIS)
## [1] 1 1 1 1 1 2 1
    # but available in the data only with 0 decimals
    getNDecimalsData(x = dataSL$DURDIS)
## [1] 1 1 1 1 1 1 1
    # The minimum of the #decimals based on the data and pre-defined rule is:
    getNDecimals(x = dataSL$DURDIS)
## [1] 1 1 1 1 1 1 1
    ## Take the maximum number of decimals 
    getMaxNDecimals(x = dataSL$DURDIS)
## [1] 1
    ## Custom set of statistics are extracted when x is specified:
    getStats(x = dataSL$DURDIS)
## $n
## roundHalfUpTextFormat(statN, 0)
## 
## $Mean
## roundHalfUpTextFormat(statMean, 2)
## 
## $SD
## roundHalfUpTextFormat(statSD, 2)
## 
## $SE
## roundHalfUpTextFormat(statSE, 3)
## 
## $Median
## roundHalfUpTextFormat(statMedian, 2)
## 
## $Min
## roundHalfUpTextFormat(statMin, 1)
## 
## $Max
## roundHalfUpTextFormat(statMax, 1)
## 
## $`%`
## (function (x, nDec = 1) 
## {
##     xRF <- ifelse(is.na(x), "-", ifelse(x == 0, "0", ifelse(x == 
##         100, "100", ifelse(x < 0.1, "<0.1", ifelse(x > 99.9, 
##         ">99.9", roundHalfUpTextFormat(x, digits = nDec))))))
##     return(xRF)
## })(statPercN)
## 
## $m
## roundHalfUpTextFormat(statm, 0)
    # To fix the number of decimals:
    getStats(type = "summary", nDecCont = 1)
## $n
## roundHalfUpTextFormat(statN, 0)
## 
## $Mean
## roundHalfUpTextFormat(statMean, 2)
## 
## $SD
## roundHalfUpTextFormat(statSD, 2)
## 
## $SE
## roundHalfUpTextFormat(statSE, 3)
## 
## $Median
## roundHalfUpTextFormat(statMedian, 2)
## 
## $Min
## roundHalfUpTextFormat(statMin, 1)
## 
## $Max
## roundHalfUpTextFormat(statMax, 1)
## 
## $`%`
## (function (x, nDec = 1) 
## {
##     xRF <- ifelse(is.na(x), "-", ifelse(x == 0, "0", ifelse(x == 
##         100, "100", ifelse(x < 0.1, "<0.1", ifelse(x > 99.9, 
##         ">99.9", roundHalfUpTextFormat(x, digits = nDec))))))
##     return(xRF)
## })(statPercN)
## 
## $m
## roundHalfUpTextFormat(statm, 0)
    ## Create summary statistics table
    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "DURDIS"),
        stats = list(
            AGE = getStats(type = "median (range)", x = dataSL$AGE),
            DURDIS = getStats(type = "median (range)", x = dataSL$DURDIS)
        )
    )

Variable

Median (range)
(N=7)

AGE

75.0 (57,89)

DURDIS

31.40 (2.2,39.8)

2.6.2 Custom stats function (Advanced)

A custom function can be created to create custom statistics with fixed number of digits.

For example, the AGE is displayed with 1 digit and the height with two digits:

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL"),
        stats = list(
            AGE = list(Median = expression(roundHalfUpTextFormat(statMedian, 1))),
            HEIGHTBL = list(Median = expression(roundHalfUpTextFormat(statMedian, 2)))
        )
    )

Variable

Median
(N=7)

AGE

75.0

HEIGHTBL

158.80

To create the stats parameter for a specific number of digits, a custom function can be created:

    # wrapper function to include median with specific number of digits
    # and min/max with specified number of digits - 1
    statsDMNum <- function(digitsMin)
        list('Median (range)' = 
            bquote(paste0(
                roundHalfUpTextFormat(statMedian, .(digitsMin+1)), 
                " (", roundHalfUpTextFormat(statMin, .(digitsMin)), ",", 
                roundHalfUpTextFormat(statMax, .(digitsMin)),
                ")"
            ))
    )

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL", "WEIGHTBL", "BMIBL", "RACE", "SEX"),
        stats = list(
            AGE = statsDMNum(0),
            HEIGHTBL = statsDMNum(1),
            WEIGHTBL = statsDMNum(1),
            BMIBL = statsDMNum(1),
            RACE = getStats("n (%)"),
            SEX = getStats("n (%)")
        )
    )

Variable

StatisticValue
(N=7)

Variable group

Statistic

AGE Median (range)

75.0 (57,89)

HEIGHTBL Median (range)

158.80 (151.1,177.8)

WEIGHTBL Median (range)

63.50 (45.4,87.1)

BMIBL Median (range)

22.80 (17.7,28.3)

RACE

BLACK OR AFRICAN AMERICAN n (%)

1 (14.3)

WHITE n (%)

6 (85.7)

SEX

F n (%)

5 (71.4)

M n (%)

2 (28.6)

2.7 Statistics layout

The layout of the statistics is specified via the statsLayout parameter.

By default, the statistics are included in rows within each variable.

    # statsLayout = 'row'
    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL"),
        stats = list(Mean = expression(statMean), 'SE' = expression(statSE))
    )

Variable

StatisticValue
(N=7)

Statistic

AGE

Mean

74.29

SE

3.714

HEIGHTBL

Mean

162.2

SE

3.873

The statistics can also be included in columns.

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL"),
        stats = list(Mean = expression(statMean), 'SE' = expression(statSE)),
        statsLayout = "col"
    )

Variable

Mean

SE

AGE

74.29

3.714

HEIGHTBL

162.2

3.873

The statistics can also be specified in different rows, but in a separated column.

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL"),
        stats = list(Mean = expression(statMean), 'SE' = expression(statSE)),
        statsLayout = "rowInSepCol"
    )

Variable

Statistic

StatisticValue
(N=7)

AGE

Mean

74.29

SE

3.714

HEIGHTBL

Mean

162.2

SE

3.873

By default, if only one statistic is available in the table, the name of the statistic is not included in the rows/columns, as the statistic is generally described in this case in the title of the table.

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL"),
        stats = list(Mean = expression(statMean))
    )

Variable

Mean
(N=7)

AGE

74.29

HEIGHTBL

162.2

To include even in this case the name of the statistic, the parameter statsLabInclude should be set to TRUE.

    getSummaryStatisticsTable(
        data = dataSL,
        var = c("AGE", "HEIGHTBL"),
        stats = list(Mean = expression(statMean)),
        statsLabInclude = TRUE
    )

Variable

Mean
(N=7)

AGE

Mean

74.29

HEIGHTBL

Mean

162.2

3 Table layout

The general table layout is driven by the specification of variables to be displayed in rows (in the vertical direction) or in columns (in the horizontal direction).

If no variables are specified in var, counts across row/column variable are displayed.

The adverse events dataset is used for demonstration.

    dataAE <-  subset(dataAll$ADAE, SAFFL == "Y" & TRTEMFL == "Y")
    
    # ensure that order of elements is the one specified in 
    # the corresponding numeric variable
    dataAE$TRTA <- with(dataAE, reorder(TRTA, TRTAN))
    dataAE$AESEV <- factor(
        dataAE$AESEV, 
        levels = c("MILD", "MODERATE", "SEVERE")
    )
    
    dataAEInterest <- subset(dataAE, AESOC %in% c(
        "INFECTIONS AND INFESTATIONS",
        "GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS"
       )
    )

3.1 Row and column variables

Specific grouping variable(s) for the columns can be specified via the colVar parameter and for the rows via the rowVar parameter.

If multiple category variables are specified, they should be specified in hierarchical order.

    # unique row variable
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = "AEDECOD",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Dictionary-Derived Term

n (%)
(N=5)

APPLICATION SITE DERMATITIS

1 (20.0)

APPLICATION SITE ERYTHEMA

3 (60.0)

APPLICATION SITE IRRITATION

2 (40.0)

APPLICATION SITE PRURITUS

4 (80.0)

FATIGUE

1 (20.0)

LOWER RESPIRATORY TRACT INFECTION

1 (20.0)

PNEUMONIA

1 (20.0)

SECRETION DISCHARGE

1 (20.0)

SUDDEN DEATH

1 (20.0)

    # multiple nested row variables
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

n (%)
(N=5)

Dictionary-Derived Term

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

1 (20.0)

APPLICATION SITE ERYTHEMA

3 (60.0)

APPLICATION SITE IRRITATION

2 (40.0)

APPLICATION SITE PRURITUS

4 (80.0)

FATIGUE

1 (20.0)

SECRETION DISCHARGE

1 (20.0)

SUDDEN DEATH

1 (20.0)

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

1 (20.0)

PNEUMONIA

1 (20.0)

    # unique column variable
    getSummaryStatisticsTable(
        data = dataAEInterest,
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

2 (100)

3 (100)

    # combination of rows and columns
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars,
        colHeaderTotalInclude = FALSE
    )

Primary System Organ Class

Xanomeline Low Dose

Xanomeline High Dose

Dictionary-Derived Term

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

3.2 Row variable

By default (when outputType is set to: ‘flextable’), if multiple row variables are specified, they are considered nested and displayed in the first column of the final table. Each sub-category is indicated with a specific indent (customizable with rowVarPadBase).

3.2.1 Variable in separated column

Row variables that should be included as a separated column should be specified via the rowVarInSepCol parameter.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD", "AESEV"),
        rowVarInSepCol = "AESEV",
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Severity/Intensity

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

MILD

0

1 (33.3)

MODERATE

0

1 (33.3)

APPLICATION SITE ERYTHEMA

MILD

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

MILD

1 (50.0)

1 (33.3)

MODERATE

0

1 (33.3)

APPLICATION SITE PRURITUS

MILD

2 (100)

1 (33.3)

MODERATE

0

1 (33.3)

FATIGUE

MILD

0

1 (33.3)

SECRETION DISCHARGE

MILD

1 (50.0)

0

SUDDEN DEATH

SEVERE

1 (50.0)

0

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

MODERATE

0

1 (33.3)

PNEUMONIA

MODERATE

1 (50.0)

0

3.2.2 Row ordering

The categories in the row variables can be ordered based on the rowOrder variable.

This variable is either:

  • a string with the name of an implemented method to order the rows, among:
    • alphabetical: categories are ordered alphabetically
    • auto: categories are ordered based on the levels if the input variable is a factor, alphabetically otherwise
    • total: categories are ordered based on the ‘total’ column (see section @ref(colTotal)) (if the total column is not included in the table)
  • a custom ordering function to apply in the data to order the rows

3.2.2.1 Common order for all row variables

    # 'auto':

    # set order of SOC to reverse alphabetical order
    dataAEInterest$AESOC <- factor(
        dataAEInterest$AESOC, 
        levels = rev(sort(unique(as.character(dataAEInterest$AESOC))))
    )
    # AEDECOD is not a factor -> sort alphabetically by default
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarLab = labelVars[c("AEDECOD")],
        rowVarTotalInclude = c("AESOC", "AEDECOD"),
        colVar = "TRTA", colTotalInclude = TRUE,
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Total
(N=5)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

5 (100)

INFECTIONS AND INFESTATIONS

1 (50.0)

1 (33.3)

2 (40.0)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

1 (20.0)

PNEUMONIA

1 (50.0)

0

1 (20.0)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

2 (100)

3 (100)

5 (100)

APPLICATION SITE DERMATITIS

0

1 (33.3)

1 (20.0)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

3 (60.0)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

2 (40.0)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

4 (80.0)

FATIGUE

0

1 (33.3)

1 (20.0)

SECRETION DISCHARGE

1 (50.0)

0

1 (20.0)

SUDDEN DEATH

1 (50.0)

0

1 (20.0)

    # total counts
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarLab = labelVars[c("AEDECOD")],
        rowVarTotalInclude = c("AESOC", "AEDECOD"),
        colVar = "TRTA", colTotalInclude = TRUE, colTotalLab = "Number of subjects",
        rowOrder = "total",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Number of subjects
(N=5)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

5 (100)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

2 (100)

3 (100)

5 (100)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

4 (80.0)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

3 (60.0)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

2 (40.0)

APPLICATION SITE DERMATITIS

0

1 (33.3)

1 (20.0)

FATIGUE

0

1 (33.3)

1 (20.0)

SECRETION DISCHARGE

1 (50.0)

0

1 (20.0)

SUDDEN DEATH

1 (50.0)

0

1 (20.0)

INFECTIONS AND INFESTATIONS

1 (50.0)

1 (33.3)

2 (40.0)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

1 (20.0)

PNEUMONIA

1 (50.0)

0

1 (20.0)

    # same order even if the 'total' column is not specified
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarLab = labelVars[c("AEDECOD")],
        rowVarTotalInclude = c("AESOC", "AEDECOD"),
        colVar = "TRTA", 
        rowOrder = "total", 
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

2 (100)

3 (100)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE DERMATITIS

0

1 (33.3)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

INFECTIONS AND INFESTATIONS

1 (50.0)

1 (33.3)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

3.2.2.2 Different orders for each row variable

In case the order should be different for each row variable, a named list is provided for the rowVar parameter.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarLab = labelVars[c("AEDECOD")],
        rowVarTotalInclude = c("AESOC", "AEDECOD"),
        colVar = "TRTA", #colTotalInclude = TRUE,
        rowOrder = c(AESOC = "alphabetical", AEDECOD = "total"),
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

2 (100)

3 (100)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE DERMATITIS

0

1 (33.3)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

INFECTIONS AND INFESTATIONS

1 (50.0)

1 (33.3)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

3.2.2.3 Row order based on the total of a column category

If the row categories should be ordered by total counts for a specific category of the column variable(s), a function rowOrderTotalFilterFct is specified.

The adverse events are sorted based on the incidence in the treated group.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarLab = labelVars[c("AEDECOD")],
        rowVarTotalInclude = c("AESOC", "AEDECOD"),
        colVar = "TRTA", colTotalInclude = TRUE,
        rowOrder = "total",
        stats = getStats("n (%)"),
        labelVars = labelVars,
        # consider only the counts of the treated patients to order the rows
        rowOrderTotalFilterFct = function(x) subset(x, TRTA == "Xanomeline High Dose")
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Total
(N=5)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

5 (100)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

2 (100)

3 (100)

5 (100)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

4 (80.0)

APPLICATION SITE DERMATITIS

0

1 (33.3)

1 (20.0)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

3 (60.0)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

2 (40.0)

FATIGUE

0

1 (33.3)

1 (20.0)

SECRETION DISCHARGE

1 (50.0)

0

1 (20.0)

SUDDEN DEATH

1 (50.0)

0

1 (20.0)

INFECTIONS AND INFESTATIONS

1 (50.0)

1 (33.3)

2 (40.0)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

1 (20.0)

PNEUMONIA

1 (50.0)

0

1 (20.0)

3.2.2.4 Row order based on a custom specified function

If the method to order the rows is more complex, the rowOrder parameter specifies a function taking the summary table as input and returning the order levels of the elements in the row variable.

For example, the adverse event table is sorted based on the counts of patient presenting this event across all treatment classes, and in case of ties based on the counts of treated-patients presenting this event.

    library(plyr)
    getSummaryStatisticsTable(
        data = dataAEInterest,
        type = "count",
        rowVar = "AEHLT",
        rowOrder = function(x){
            x <- subset(x, !isTotal)
            totalAcrossTreatments <- subset(x, TRTA == "Total")
            # counts across treated patients
            totalForTreatmentOnly <- subset(x, TRTA == "Xanomeline High Dose")
            dataCounts <- merge(totalAcrossTreatments, totalForTreatmentOnly, by = "AEHLT", suffixes = c(".all", ".treat"))
            # sort first based on overall count, then counts of treated patients
            dataCounts[with(dataCounts, order(`statN.all`, `statN.treat`, decreasing = TRUE)), "AEHLT"]
        },
        colVar = "TRTA", colTotalInclude = TRUE,
        labelVars = labelVars,
        title = "Table: Adverse Events ordered based on total counts",
        stats = list(expression(paste0(statN, " (", round(statPercN, 1), ")"))),
        footer = "Statistics: n (%)"
    )

Table: Adverse Events ordered based on total counts

High Level Term

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Total
(N=5)

HLT_0317

2 (100)

2 (66.7)

4 (80)

HLT_0617

2 (100)

1 (33.3)

3 (60)

HLT_0061

1 (50)

1 (33.3)

2 (40)

HLT_0043

0 (0)

1 (33.3)

1 (20)

HLT_0052

0 (0)

1 (33.3)

1 (20)

HLT_0343

0 (0)

1 (33.3)

1 (20)

HLT_0142

1 (50)

0 (0)

1 (20)

HLT_0251

1 (50)

0 (0)

1 (20)

HLT_0683

1 (50)

0 (0)

1 (20)

Statistics: n (%)

The adverse event table is now ordered based on the counts in the placebo, then treated-patients column, for the organ class and the adverse event term separately.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarLab = labelVars[c("AEDECOD")],
        rowVarTotalInclude = c("AESOC", "AEDECOD"),
        colVar = "TRTA", colTotalInclude = TRUE,
        rowOrder = list(
            AESOC = function(table) {
                # records with total for each AESOC
                nAESOCPlacebo <- subset(table, !isTotal & grepl("placebo", TRTA) & AEDECOD == "Total")
                nAESOCTreat <- subset(table, !isTotal & grepl("High Dose", TRTA) & AEDECOD == "Total")
                nAESOCDf <- merge(nAESOCPlacebo, nAESOCTreat, by = "AESOC", suffixes = c(".placebo", ".treatment"))
                nAESOCDf[with(nAESOCDf, order(`statN.placebo`, `statN.treatment`, decreasing = TRUE)), "AESOC"]
            },
            AEDECOD = function(table) {
                # records with counts for each AEDECOD
                nAEDECODPlacebo <- subset(table, !isTotal & grepl("placebo", TRTA) & AEDECOD != "Total")
                nAEDECODTreat <- subset(table, !isTotal & grepl("High Dose", TRTA) & AEDECOD != "Total")
                nAEDECODDf <- merge(nAEDECODPlacebo, nAEDECODTreat, by = "AEDECOD", suffixes = c(".placebo", ".treatment"))
                nAEDECODDf[with(nAEDECODDf, order(`statN.placebo`, `statN.treatment`, decreasing = TRUE)), "AEDECOD"]
            }
        ),
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Total
(N=5)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

5 (100)

INFECTIONS AND INFESTATIONS

1 (50.0)

1 (33.3)

2 (40.0)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

1 (20.0)

PNEUMONIA

1 (50.0)

0

1 (20.0)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

2 (100)

3 (100)

5 (100)

APPLICATION SITE DERMATITIS

0

1 (33.3)

1 (20.0)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

3 (60.0)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

2 (40.0)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

4 (80.0)

FATIGUE

0

1 (33.3)

1 (20.0)

SECRETION DISCHARGE

1 (50.0)

0

1 (20.0)

SUDDEN DEATH

1 (50.0)

0

1 (20.0)

3.2.3 Row variable labels

3.2.3.1 Based on dataset

The labels used for the variables parameter (row variables) are automatically extracted from the labels contained in the SAS dataset, by specifying the labelVars parameter.

    # combination of rows and columns
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

3.2.3.2 Custom

The label can also be specified directly via the rowVarLab parameter, for each variable in rowVar.

If an unique row label should be used (even if multiple row variables are specified), rowVarLab is set to this unique label.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        stats = getStats("n (%)"),
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        labelVars = labelVars
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

3.2.4 Inclusion of row/column categories not available in the data

As for the variable to summarize, to include categories in the row or column variables not available in the data, these variables should be formatted as a factor with categories specified in its levels.

Furthermore, the parameters rowInclude0 and colInclude0 should be set to TRUE to include counts for empty categories within the row/column.

    ## only consider a subset of adverse events
    dataAESubset <- subset(dataAE, AEHLT == "HLT_0617")
    
    ## create dummy categories for:
    # treatment
    dataAESubset$TRTA <- with(dataAESubset, 
        factor(TRTA, levels = c(unique(as.character(TRTA)), "Treatment B"))
    )
    # low-level term category
    dataAESubset$AELLT <- with(dataAESubset, 
        factor(AELLT, levels = c(unique(as.character(AELLT)), "Lymphocyte percentage increased"))
    )
    
    # create summary statistics table
    getSummaryStatisticsTable(
        data = dataAESubset,
        type = "count",
        rowVar = c("AEHLT", "AELLT"),
        rowInclude0 = TRUE, colInclude0 = TRUE,
        colVar = "TRTA",
        labelVars = labelVars,
        title = "Table: Adverse Events: white blood cell analyses",
        stats = getStats("n (%)"),
        footer = "Statistics: n (%)"
    )

Table: Adverse Events: white blood cell analyses

High Level Term

Xanomeline High Dose
(N=1)

Xanomeline Low Dose
(N=2)

Treatment B
(N=0)

Lowest Level Term

HLT_0617

APPLICATION SITE REDNESS

1 (100)

2 (100)

-

Lymphocyte percentage increased

0

0

-

Statistics: n (%)

3.3 Variable(s) to summarize

3.3.1 Default

The variable(s) used for the summary statistics (var) are included by default in rows.

    dataDIABP <- subset(dataAll$ADVS, 
        SAFFL == "Y" & ANL01FL == "Y" &
        PARAMCD == "DIABP" & 
        AVISIT %in% c("Baseline", "Week 8") &
        ATPT == "AFTER LYING DOWN FOR 5 MINUTES"
    )
    dataDIABP$TRTA <- reorder(dataDIABP$TRTA, dataDIABP$TRTAN)
    dataDIABP$AVISIT <- reorder(dataDIABP$AVISIT, dataDIABP$AVISITN)
    
    getSummaryStatisticsTable(
        data = dataDIABP,
        var = c("AVAL", "CHG"),
        colVar = "TRTA",
        rowVar = "AVISIT",
        labelVars = labelVars,
        stats = getStats("summary-default")
    )

Analysis Visit

Placebo
(N=2)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Variable

Statistic

Baseline

Analysis Value

n

2

2

3

Mean

71

74

68.33

SD

1.414

14.14

11.59

SE

1

10

6.692

Median

71

74

70

Min

70

64

56

Max

72

84

79

Change from Baseline

n

2

2

3

Mean

0

0

0

SD

0

0

0

SE

0

0

0

Median

0

0

0

Min

0

0

0

Max

0

0

0

Week 8

Analysis Value

n

1

2

3

Mean

72

61.5

68

SD

NA

3.536

7

SE

NA

2.5

4.041

Median

72

61.5

68

Min

72

59

61

Max

72

64

75

Change from Baseline

n

1

2

3

Mean

0

-12.5

-0.3333

SD

NA

10.61

9.238

SE

NA

7.5

5.333

Median

0

-12.5

5

Min

0

-20

-11

Max

0

-5

5

3.3.2 Summary variable in columns

In case multiple variables are to be summarized, the different variables can be included in different columns by including the specific label: ‘variable’ in colVar. Beware that such layout only makes sense for variables with similar types (e.g. all numeric variables).

getSummaryStatisticsTable(
    data = dataDIABP,
    var = c("AVAL", "CHG"),
    colVar = c("variable", "TRTA"),
    rowVar = "AVISIT",
    labelVars = labelVars,
    stats = getStats("summary-default")
)

Analysis Visit

Analysis Value

Change from Baseline

Statistic

Placebo
(N=2)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Placebo
(N=2)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Baseline

n

2

2

3

2

2

3

Mean

71

74

68.33

0

0

0

SD

1.414

14.14

11.59

0

0

0

SE

1

10

6.692

0

0

0

Median

71

74

70

0

0

0

Min

70

64

56

0

0

0

Max

72

84

79

0

0

0

Week 8

n

1

2

3

1

2

3

Mean

72

61.5

68

0

-12.5

-0.3333

SD

NA

3.536

7

NA

10.61

9.238

SE

NA

2.5

4.041

NA

7.5

5.333

Median

72

61.5

68

0

-12.5

5

Min

72

59

61

0

-20

-11

Max

72

64

75

0

-5

5

3.3.3 Inclusion of summary variables in case one variable is specified

By default, the variable label is not included if only one summary statistic variable is specified.

    getSummaryStatisticsTable(data = dataSL, var = "AGE", colVar = "TRT01P")

Statistic

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

statN

2

3

2

statm

2

3

2

statMean

82

66.67

78

statSD

9.899

8.737

2.828

statSE

7

5.044

2

statMedian

82

69

78

statMin

75

57

76

statMax

89

74

80

statPercTotalN

2

3

2

statPercN

100

100

100

To include the label in case only one summary statistic variable is specified, the parameter varLabInclude should be set to TRUE.

    getSummaryStatisticsTable(
        data = dataSL, 
        var = "AGE", 
        varLabInclude = TRUE,
        colVar = "TRT01P"
    )

Variable

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

Statistic

AGE

statN

2

3

2

statm

2

3

2

statMean

82

66.67

78

statSD

9.899

8.737

2.828

statSE

7

5.044

2

statMedian

82

69

78

statMin

75

57

76

statMax

89

74

80

statPercTotalN

2

3

2

statPercN

100

100

100

3.4 Inclusion of the counts per group in case of missing values

It might be of interest to display the counts of all subjects per row/column variable in association of the summary statistic of a variable of interest.

For example it could be of interest to report the total number of subjects per group, which could differ from the total number of subjects for a variable of interest if this variable contain missing values.

    dataAEInterest$AESEVN <- ifelse(dataAEInterest$AESEV == "MILD", 1, 2)
    dataAEInterestWC <- ddply(dataAEInterest, c("AEDECOD", "USUBJID", "TRTA"), function(x) {
        x[which.max(x$AESEVN), ]
    })
    dataAEInterestWC[1, "AESEV"] <- NA
    getSummaryStatisticsTable(
        data = dataAEInterestWC,
        colVar = "TRTA",
        rowVar = "AEBODSYS",
        stats = getStats("n (%)"),
        var = c("AESEV", "all"),
        labelVars = labelVars
    )

Body System or Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Variable

Variable group

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

Severity/Intensity

MILD

2 (100)

2 (66.7)

MODERATE

-

2 (66.7)

SEVERE

1 (50.0)

-

all

2 (100)

3 (100)

INFECTIONS AND INFESTATIONS

Severity/Intensity MODERATE

1 (50.0)

1 (33.3)

all

1 (50.0)

1 (33.3)

4 Total

The summary table contains different types of total:

  • total used for the percentage computation displayed in the table.
    For example: report percentage of subjects with specific adverse event.
  • total reported in the column header
    For example: total number of subjects for a specific treatment arm.
  • total across rows, reported in the row header
    For example: to report percentage of subjects with adverse events in a specific body system (across adverse events).
  • total across columns, reported in a separated column
    For example: to report summary statistics across all treatments arms.

By default, the totals are extracted based on the input data, but separated datasets can be specified for the header, percentage computation, row or column total.

4.1 Summary

The different types of total of the summary table are summarized below:

Type Inclusion in the table Dataset: parameter name Dataset: default
Total in the column header Yes by default
removed if colHeaderTotalInclude = FALSE
dataTotal data for table content
dataTotalCol for total column
Total for the percentage Only if percentage requested in stats dataTotalPerc dataTotal for table content
dataTotalCol for total column
(for ‘total’ if specified as a list)
Total across rows Not by default
for specified row variable with rowVarTotalInclude
dataTotalRow data for table content
dataTotalCol for total column
(for ‘total’ if specified as a list)
Total across columns Not by default
only if colTotalInclude = TRUE
dataTotalCol data

4.2 Total for the column header

4.2.1 Current datasset

By default, the total reported in the total header is extracted from the available number of subjects in the input data.

For example, the total number of patients per treatment arm is extracted from the subject-level (ADSL) dataset.

    # by default, total number of subjects extracted from data
    getSummaryStatisticsTable(
        data = subset(dataAEInterest, AESOC == "INFECTIONS AND INFESTATIONS"),
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        stats = getStats("n (%)"),
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        labelVars = labelVars
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline Low Dose
(N=1)

Xanomeline High Dose
(N=1)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (100)

PNEUMONIA

1 (100)

0

4.2.2 External dataset

If the total should be extracted from a different dataset, it should be specified via the dataTotal variable. Please note that by default dataTotal is also used for the computation of the percentage.

    # dataset used to extract the 'Total'
    dataTotalAE <- subset(dataAll$ADSL, SAFFL == "Y")
    # should contain columns specified in 'colVar'
    dataTotalAE$TRTA <- dataTotalAE$TRT01A 

    getSummaryStatisticsTable(
        data = subset(dataAEInterest, AESOC == "INFECTIONS AND INFESTATIONS"),
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        stats = getStats("n (%)"),
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        dataTotal = dataTotalAE,
        labelVars = labelVars
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

4.3 Remove total in column header

The total number of subjects in each column is by default included. This is not displayed if colHeaderTotalInclude is set to FALSE.

    getSummaryStatisticsTable(
        data = subset(dataAEInterest, AESOC == "INFECTIONS AND INFESTATIONS"),
        rowVar = c("AESOC", "AEDECOD"),
        rowVarTotalInclude = "AEDECOD",
        rowVarTotalInSepRow = "AEDECOD",
        colVar = "TRTA",
        stats = getStats("n (%)"),
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        dataTotal = dataTotalAE,
        labelVars = labelVars,
        colHeaderTotalInclude = FALSE
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline Low Dose

Xanomeline High Dose

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

Total

1 (50.0)

1 (33.3)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

4.4 Percentage

4.4.1 Dataset

A different dataset used for the computation of the percentage can be specified via the dataTotalPerc parameter.

    getSummaryStatisticsTable(
        data = subset(dataAEInterest, AESOC == "INFECTIONS AND INFESTATIONS"),
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        stats = getStats("n (%)"),
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        dataTotalPerc = dataTotalAE,
        labelVars = labelVars
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline Low Dose
(N=1)

Xanomeline High Dose
(N=1)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

Please note that by default, if dataTotalPerc is specified, but not dataTotal, counts reported in the column header are still extracted from data.

4.4.2 Variables to compute percentage by

If the total number of subjects differ between the components of the table, the extra row/column(s) variable(s) are specified via colVarTotalPerc/rowVarTotalPerc.

For example, in a table of laboratory measurements per reference range (laboratory abnormalities): the total number of subjects for the computation of the percentage are extracted based on the number of subjects with available measurements per visit.

    dataLB <- subset(dataAll$ADLBC, 
        SAFFL == "Y" & 
        PARAMCD %in% c("K", "CHOL") &
        grepl("(Baseline)|(Week 20)", AVISIT)
    )
    dataLB$AVISIT <- with(dataLB, reorder(trimws(AVISIT), AVISITN))
    
    # counts versus the total per actual treatment arm
    getSummaryStatisticsTable(
        data = dataLB,
        colVar = "TRTA", 
        rowVar = c("PARAM", "AVISIT"), 
        var = "LBNRIND",
        stats = getStats("n (%)"),
        rowAutoMerge = FALSE, emptyValue = "0",
    )

PARAM

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

AVISIT

Variable group

Cholesterol (mmol/L)

Baseline

HIGH

1 (50.0)

0

0

NORMAL

1 (50.0)

3 (100)

2 (100)

Week 20

NORMAL

1 (50.0)

1 (33.3)

1 (50.0)

Potassium (mmol/L)

Baseline

NORMAL

2 (100)

3 (100)

2 (100)

Week 20

NORMAL

0

1 (33.3)

1 (50.0)

    # percentage based on total number of subjects with available
    # measurement at specific visit for each parameter
    getSummaryStatisticsTable(
        data = dataLB,
        colVar = "TRTA", 
        rowVar = c("PARAM", "AVISIT"), 
        rowVarTotalPerc = c("PARAM", "AVISIT"),
        var = "LBNRIND",
        stats = getStats("n (%)"),
        rowAutoMerge = FALSE, emptyValue = "0",
    )   

PARAM

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

AVISIT

Variable group

Cholesterol (mmol/L)

Baseline

HIGH

1 (50.0)

0

0

NORMAL

1 (50.0)

3 (100)

2 (100)

Week 20

NORMAL

1 (100)

1 (100)

1 (100)

Potassium (mmol/L)

Baseline

NORMAL

2 (100)

3 (100)

2 (100)

Week 20

NORMAL

0

1 (100)

1 (100)

Please note the different percentage for the number of patients with normal cholesterol measurements at week 20 between the two tables.

4.4.3 Percentage of the number of records

By default, the percentage is based on the number of subjects.

If the percentage should be computed based on the number of records instead, the parameter: statsPerc should be set to statm (statN by default).

For example, to extract the percentage of laboratory measurements by reference range and parameter:

getSummaryStatisticsTable(
    data = dataLB,
    colVar = "TRTA", 
    rowVar = c("PARAM", "AVISIT"), 
    rowVarTotalPerc = c("PARAM", "AVISIT"),
    var = "LBNRIND", 
    stats = getStats("m (%)"),
    statsPerc = "statm",
    rowAutoMerge = FALSE, emptyValue = "0",
)

PARAM

Placebo
(N=2)

Xanomeline High Dose
(N=3)

Xanomeline Low Dose
(N=2)

AVISIT

Variable group

Cholesterol (mmol/L)

Baseline

HIGH

1 (50.0)

0

0

NORMAL

1 (50.0)

3 (100)

2 (100)

Week 20

NORMAL

1 (100)

1 (100)

1 (100)

Potassium (mmol/L)

Baseline

NORMAL

2 (100)

3 (100)

2 (100)

Week 20

NORMAL

0

1 (100)

1 (100)

4.5 Total across columns

4.5.1 Inclusion

The total across all columns is included if the colTotalInclude is set to TRUE.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        colTotalInclude = TRUE, 
        stats = getStats("n (%)"),
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        dataTotal = dataTotalAE,
        labelVars = labelVars
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Total
(N=7)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

1 (14.3)

PNEUMONIA

1 (50.0)

0

1 (14.3)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

1 (14.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

3 (42.9)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

2 (28.6)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

4 (57.1)

FATIGUE

0

1 (33.3)

1 (14.3)

SECRETION DISCHARGE

1 (50.0)

0

1 (14.3)

SUDDEN DEATH

1 (50.0)

0

1 (14.3)

By default, the total number of subjects is extracted based on the input dataset across columns: subjects presenting the same event in multiple column(s) are counted once in the column total (e.g. for adverse event table in a context of cross-over experiment).

4.5.2 Label

This column is by default labelled ‘Total’, but this can be customized with the colTotalLab parameter.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        colTotalInclude = TRUE, colTotalLab = "All subjects",
        stats = getStats("n (%)"),
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        dataTotal = dataTotalAE,
        labelVars = labelVars
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

All subjects
(N=7)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

1 (14.3)

PNEUMONIA

1 (50.0)

0

1 (14.3)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

1 (14.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

3 (42.9)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

2 (28.6)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

4 (57.1)

FATIGUE

0

1 (33.3)

1 (14.3)

SECRETION DISCHARGE

1 (50.0)

0

1 (14.3)

SUDDEN DEATH

1 (50.0)

0

1 (14.3)

4.5.3 Dataset

A different dataset for the total column can also be specified via the dataTotalCol parameter.

For example, the table is restricted to only the treatment arm, but both arms are considered in the total column:

    getSummaryStatisticsTable(
        data = subset(dataAEInterest, grepl("High Dose", TRTA)),
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        colTotalInclude = TRUE, colTotalLab = "Placebo and treatment arm",
        dataTotalCol = dataAEInterest,
        stats = getStats("n (%)"), emptyValue = "0",
        rowVarLab = c(
            'AESOC' = "TEAE by SOC and Preferred Term\nn (%)"
        ),
        dataTotal = dataTotalAE,
        labelVars = labelVars
    )

TEAE by SOC and Preferred Term
n (%)

Xanomeline High Dose
(N=3)

Placebo and treatment arm
(N=7)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

1 (33.3)

1 (14.3)

PNEUMONIA

0

1 (14.3)

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

1 (33.3)

1 (14.3)

APPLICATION SITE ERYTHEMA

1 (33.3)

3 (42.9)

APPLICATION SITE IRRITATION

1 (33.3)

2 (28.6)

APPLICATION SITE PRURITUS

2 (66.7)

4 (57.1)

FATIGUE

1 (33.3)

1 (14.3)

SECRETION DISCHARGE

0

1 (14.3)

SUDDEN DEATH

0

1 (14.3)

4.6 Total across rows

4.6.1 Inclusion

If the total should be included across elements of specific rowVar variable(s), this(these) variable(s) should be included in rowVarTotalInclude.

    # total reported across AESOC
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarTotalInclude = "AESOC", 
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

    # total reported across AESOC and across AEDECOD
    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarTotalInclude = c("AESOC", "AEDECOD"), 
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

Any Primary System Organ Class, Dictionary-Derived Term

2 (100)

3 (100)

INFECTIONS AND INFESTATIONS

1 (50.0)

1 (33.3)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

2 (100)

3 (100)

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

In case multiple row variables are specified, the total can also be included for each of this variable. In this case, the total is by default included in the header of each category of this variable.

4.6.2 Label

For the first row variable, the total is included in the first row of the table, with the label specified in rowTotalLab.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarTotalInclude = "AESOC", rowTotalLab = "Any AE", 
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

Any AE

2 (100)

3 (100)

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

4.6.3 Inclusion as separated category

The row total can also be included as a separated category (‘Total’) in the table, if this variable is additionally specified in rowVarTotalInSepRow.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        rowVarTotalInclude = "AEDECOD",
        rowVarTotalInSepRow = "AEDECOD",
        colVar = "TRTA",
        stats = getStats("n (%)"),
        labelVars = labelVars
    )

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

Total

1 (50.0)

1 (33.3)

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

Total

2 (100)

3 (100)

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

4.6.4 Dataset

A different dataset considered for the row total is specified via the dataTotalRow parameter.

Different datasets can also be specified for each row variable separately (via a named list).

For example, the worst-case severity per adverse event, per and across system organ classes are displayed in the table below.

    dataAEInterest$AESEVN <- as.numeric(dataAEInterest$AESEV)
    
    # compute worst-case scenario per subject*AE term*treatment
    dataAEInterestWC <- ddply(dataAEInterest, c("AESOC", "AEDECOD", "USUBJID", "TRTA"), function(x){
        x[which.max(x$AESEVN), ]
    })

    ## 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 visit (across AEDECOD)
        'AEDECOD' = ddply(dataAEInterest, c("AESOC", "USUBJID", "TRTA"), function(x){   
            x[which.max(x$AESEVN), ]
        }),
        # across visits
        'AESOC' = ddply(dataAEInterest, c("USUBJID", "TRTA"), function(x){  
            x[which.max(x$AESEVN), ]
        })
    )

    getSummaryStatisticsTable(
        data = dataAEInterestWC,
        ## row variables:
        rowVar = c("AESOC", "AEDECOD", "AESEV"), 
        rowVarInSepCol = "AESEV",
        # total for column header and denominator
        dataTotal = dataTotalAE, 
        # include total across SOC and across AEDECOD
        rowVarTotalInclude = c("AESOC", "AEDECOD"), 
        # data for total row
        dataTotalRow = dataTotalRow, 
        # count for each severity category for the total
        rowVarTotalByVar = "AESEV", 
        rowTotalLab = "Any TEAE", 
        rowVarLab = c(AESOC = "Subjects with, n(%):", AESEV = "Worst-case scenario"),
        # sort per total in the total column
        rowOrder = "total", 
        ## column variables
        colVar = "TRTA", 
        stats = getStats("n (%)"),
        emptyValue = "0",
        labelVars = labelVars
    )

Subjects with, n(%):

Worst-case scenario

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

Any TEAE

MODERATE

1 (50.0)

3 (100)

SEVERE

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

MODERATE

0

2 (66.7)

SEVERE

1 (50.0)

0

MILD

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

MODERATE

0

1 (33.3)

MILD

2 (100)

1 (33.3)

APPLICATION SITE ERYTHEMA

MILD

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

MODERATE

0

1 (33.3)

MILD

1 (50.0)

0

APPLICATION SITE DERMATITIS

MODERATE

0

1 (33.3)

FATIGUE

MILD

0

1 (33.3)

SECRETION DISCHARGE

MILD

1 (50.0)

0

SUDDEN DEATH

SEVERE

1 (50.0)

0

INFECTIONS AND INFESTATIONS

MODERATE

1 (50.0)

1 (33.3)

LOWER RESPIRATORY TRACT INFECTION

MODERATE

0

1 (33.3)

PNEUMONIA

MODERATE

1 (50.0)

0

5 Labels

If the data is loaded into R with the read_haven of the haven package, or the loadDataADaMSDTM function of the clinUtils package, the label for each variable is stored in the ‘label’ attribute of the corresponding column.

However, if this label is lost (e.g. if the object is subsetted), labels can be specified via the labelVars parameter for all variables at once, or via specific [parameter]Lab parameter, as rowVarLab/colVarLab/varLab for the row/column/variable to summarize respectively.

6 Title and footnote

Title and footnote are specified via the corresponding title and footer parameters. The convenient function toTitleCase from the tools package is used to set title case for the title of the summary statistics table.

    getSummaryStatisticsTable(
        data = dataAEInterest,
        rowVar = c("AESOC", "AEDECOD"),
        colVar = "TRTA",
        stats = getStats("n (%)"),
        dataTotal = dataTotalAE,
        labelVars = labelVars,
        title = toTitleCase("MOR106-CL-102: Adverse Events by System Organ Class and Preferred Term (Safety Analysis Set, Part 1)"),
        footer = c(
            "N=number of subjects with data; n=number of subjects with this observation",
            "Denominator for percentage calculations = the total number of subjects per treatment group in the safety population"
        )
    )

MOR106-CL-102: Adverse Events by System Organ Class and Preferred Term (Safety Analysis Set, Part 1)

Primary System Organ Class

Xanomeline Low Dose
(N=2)

Xanomeline High Dose
(N=3)

Dictionary-Derived Term

INFECTIONS AND INFESTATIONS

LOWER RESPIRATORY TRACT INFECTION

0

1 (33.3)

PNEUMONIA

1 (50.0)

0

GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS

APPLICATION SITE DERMATITIS

0

1 (33.3)

APPLICATION SITE ERYTHEMA

2 (100)

1 (33.3)

APPLICATION SITE IRRITATION

1 (50.0)

1 (33.3)

APPLICATION SITE PRURITUS

2 (100)

2 (66.7)

FATIGUE

0

1 (33.3)

SECRETION DISCHARGE

1 (50.0)

0

SUDDEN DEATH

1 (50.0)

0

N=number of subjects with data; n=number of subjects with this observation

Denominator for percentage calculations = the total number of subjects per treatment group in the safety population

7 Appendix

7.1 Session information

R version 4.4.0 (2024-04-24)

Platform: x86_64-pc-linux-gnu

locale: C

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

other attached packages: plyr(v.1.8.9), pander(v.0.6.5), clinUtils(v.0.2.0), inTextSummaryTable(v.3.3.3) and knitr(v.1.47)

loaded via a namespace (and not attached): gtable(v.0.3.5), xfun(v.0.44), bslib(v.0.7.0), ggplot2(v.3.5.1), htmlwidgets(v.1.6.4), ggrepel(v.0.9.5), vctrs(v.0.6.5), crosstalk(v.1.2.1), generics(v.0.1.3), curl(v.5.2.1), tibble(v.3.2.1), fansi(v.1.0.6), highr(v.0.11), pkgconfig(v.2.0.3), data.table(v.1.15.4), uuid(v.1.2-0), lifecycle(v.1.0.4), flextable(v.0.9.6), farver(v.2.1.2), stringr(v.1.5.1), compiler(v.4.4.0), textshaping(v.0.4.0), munsell(v.0.5.1), httpuv(v.1.6.15), fontquiver(v.0.2.1), fontLiberation(v.0.1.0), htmltools(v.0.5.8.1), sass(v.0.4.9), yaml(v.2.3.8), later(v.1.3.2), pillar(v.1.9.0), crayon(v.1.5.2), jquerylib(v.0.1.4), gfonts(v.0.2.0), openssl(v.2.2.0), DT(v.0.33), cachem(v.1.1.0), mime(v.0.12), fontBitstreamVera(v.0.1.1), tidyselect(v.1.2.1), zip(v.2.3.1), digest(v.0.6.35), stringi(v.1.8.4), reshape2(v.1.4.4), dplyr(v.1.1.4), labeling(v.0.4.3), forcats(v.1.0.0), cowplot(v.1.1.3), fastmap(v.1.2.0), grid(v.4.4.0), colorspace(v.2.1-0), cli(v.3.6.2), magrittr(v.2.0.3), crul(v.1.4.2), utf8(v.1.2.4), withr(v.3.0.0), gdtools(v.0.3.7), scales(v.1.3.0), promises(v.1.3.0), rmarkdown(v.2.27), officer(v.0.6.6), askpass(v.1.2.0), ragg(v.1.3.2), hms(v.1.1.3), shiny(v.1.8.1.1), evaluate(v.0.24.0), haven(v.2.5.4), viridisLite(v.0.4.2), rlang(v.1.1.4), Rcpp(v.1.0.12), xtable(v.1.8-4), glue(v.1.7.0), httpcode(v.0.3.0), xml2(v.1.3.6), jsonlite(v.1.8.8), R6(v.2.5.1) and systemfonts(v.1.1.0)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.