# Example Clinical Trials Tables

#### Gabriel Becker and Adrian Waddell

#### 2021-10-06

## Introduction

In this vignette we create a

- demographic table
- adverse event table
- response table

using the `rtables` layout facility. That is, we demonstrate how the layout based tabulation framework can specify the structure and relations that are commonly found when analyzing clinical trials data.

Note that all the data is created using random number generators. All `ex_*` data which is currently attached to the `rtables` package were created using `random.cdisc.data` another R package that we intend to release as open source soon.

The packages used in this vignette are:

    library(rtables)
    library(tibble)
    library(dplyr)

## Demographic Table

Demographic tables summarize the variables content for different population subsets (encoded in the columns).

One feature of `analyze` that we have not introduced in the previous vignette is that the analysis function `afun` can specify multiple rows with the `in_rows` function:

    ADSL <- ex_adsl  # Example ADSL dataset

    basic_table() %>%
      split_cols_by("ARM") %>%
      analyze(vars = "AGE", afun = function(x) {
        in_rows(
          "Mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
          "Range" = rcell(range(x), format = "xx.xx - xx.xx")
        )
      }) %>%
      build_table(ADSL)

                 A: Drug X     B: Placebo    C: Combination
    -------------------------------------------------------
    Mean (sd)   33.77 (6.55)   35.43 (7.9)    35.43 (7.72)
    Range         21 - 50        21 - 62        20 - 69

Multiple variables can be analyzed in one `analyze` call:

    basic_table() %>%
      split_cols_by("ARM") %>%
      analyze(vars = c("AGE", "BMRKR1"), afun = function(x) {
        in_rows(
          "Mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
          "Range" = rcell(range(x), format = "xx.xx - xx.xx")
        )
      }) %>%
      build_table(ADSL)

                   A: Drug X      B: Placebo    C: Combination
    ----------------------------------------------------------
    AGE
      Mean (sd)   33.77 (6.55)   35.43 (7.9)     35.43 (7.72)
      Range         21 - 50        21 - 62         20 - 69
    BMRKR1
      Mean (sd)   5.97 (3.55)     5.7 (3.31)     5.62 (3.49)
      Range       0.41 - 17.67   0.65 - 14.24    0.17 - 21.39

Hence, if `afun` can process different data vector types (i.e. variables selected from the data) then we are fairly close to a standard demographic table. Here is a function that either creates a count table or some number summary if the argument `x` is a factor or numeric, respectively:

    s_summary <- function(x) {
      if (is.numeric(x)) {
        in_rows(
          "n" = rcell(sum(!is.na(x)), format = "xx"),
          "Mean (sd)" = rcell(c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE)), format = "xx.xx (xx.xx)"),
          "IQR" = rcell(IQR(x, na.rm = TRUE), format = "xx.xx"),
          "min - max" = rcell(range(x, na.rm = TRUE), format = "xx.xx - xx.xx")
        )
      } else if (is.factor(x)) {

        vs <- as.list(table(x))
        do.call(in_rows, lapply(vs, rcell, format = "xx"))

      } else (
        stop("type not supported")
      )
    }

Note we use `rcell`s to wrap the results in order to add formatting instructions for `rtables`. We can use `s_summary` outside the context of tabulation:

    s_summary(ADSL$AGE)

    RowsVerticalSection (in_rows) object print method:
    ----------------------------
       row_name formatted_cell indent_mod row_label
    1         n            400          0         n
    2 Mean (sd)   34.88 (7.44)          0 Mean (sd)
    3       IQR             10          0       IQR
    4 min - max        20 - 69          0 min - max

and

    s_summary(ADSL$SEX)

    RowsVerticalSection (in_rows) object print method:
    ----------------------------
              row_name formatted_cell indent_mod        row_label
    1                F            222          0                F
    2                M            166          0                M
    3                U              9          0                U
    4 UNDIFFERENTIATED              3          0 UNDIFFERENTIATED

We can now create a commonly used variant of the demographic table:

    lyt <- basic_table() %>%
      split_cols_by(var = "ARM") %>%
      analyze(c("AGE", "SEX"), afun = s_summary)

    tbl <- build_table(lyt, ADSL)
    tbl

                          A: Drug X     B: Placebo    C: Combination
    ----------------------------------------------------------------
    AGE
      n                      134            134            132
      Mean (sd)          33.77 (6.55)   35.43 (7.9)    35.43 (7.72)
      IQR                     11            10              10
      min - max            21 - 50        21 - 62        20 - 69
    SEX
      F                       79            77              66
      M                       51            55              60
      U                       3              2              4
      UNDIFFERENTIATED        1              0              2

Note that `analyze` can also be called multiple times in sequence:

    tbl2 <- basic_table() %>%
      split_cols_by(var = "ARM") %>%
      analyze("AGE", s_summary) %>%
      analyze("SEX", s_summary) %>%
      build_table(ADSL)

    tbl2

                          A: Drug X     B: Placebo    C: Combination
    ----------------------------------------------------------------
    AGE
      n                      134            134            132
      Mean (sd)          33.77 (6.55)   35.43 (7.9)    35.43 (7.72)
      IQR                     11            10              10
      min - max            21 - 50        21 - 62        20 - 69
    SEX
      F                       79            77              66
      M                       51            55              60
      U                       3              2              4
      UNDIFFERENTIATED        1              0              2

which leads to the identical table as `tbl`:

    identical(tbl, tbl2)

    [1] TRUE

In clinical trials analyses the number of patients per column is often referred to as `N` (rather than the overall population which outside of clinical trials is commonly referred to as `N`). Column `N`s are added using the `add_colcounts` function:

    basic_table() %>%
      split_cols_by(var = "ARMCD") %>%
      add_colcounts() %>%
      analyze(c("AGE", "SEX"), s_summary) %>%
      build_table(ADSL)

                            ARM A          ARM B         ARM C
                           (N=134)        (N=134)       (N=132)
    --------------------------------------------------------------
    AGE
      n                      134            134           132
      Mean (sd)          33.77 (6.55)   35.43 (7.9)   35.43 (7.72)
      IQR                     11            10             10
      min - max            21 - 50        21 - 62       20 - 69
    SEX
      F                       79            77             66
      M                       51            55             60
      U                       3              2             4
      UNDIFFERENTIATED        1              0             2

### Variations on the demographic table

We will now show a couple of variations of the demographic table that we developed above. These variations are in structure and not in analysis, hence they don’t require a modification to the `s_summary` function.

We will start with a standard table analyzing the variables `AGE` and `BMRKR2` variables:

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      analyze(c("AGE", "BMRKR2"), s_summary) %>%
      build_table(ADSL)

                   A: Drug X     B: Placebo    C: Combination
                    (N=134)        (N=134)        (N=132)
    ---------------------------------------------------------
    AGE
      n               134            134            132
      Mean (sd)   33.77 (6.55)   35.43 (7.9)    35.43 (7.72)
      IQR              11            10              10
      min - max     21 - 50        21 - 62        20 - 69
    BMRKR2
      LOW              50            45              40
      MEDIUM           37            56              42
      HIGH             47            33              50

Assume we would like to have this analysis carried out per gender encoded in the row space:

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX") %>%
      analyze(c("AGE", "BMRKR2"), s_summary) %>%
      build_table(ADSL)

                        A: Drug X      B: Placebo    C: Combination
                         (N=134)        (N=134)         (N=132)
    ---------------------------------------------------------------
    F
      AGE
        n                   79             77              66
        Mean (sd)      32.76 (6.09)   34.12 (7.06)    35.2 (7.43)
        IQR                 9              8              6.75
        min - max        21 - 47        23 - 58         21 - 64
      BMRKR2
        LOW                 26             21              26
        MEDIUM              21             38              17
        HIGH                32             18              23
    M
      AGE
        n                   51             55              60
        Mean (sd)      35.57 (7.08)   37.44 (8.69)    35.38 (8.24)
        IQR                 11             9               11
        min - max        23 - 50        21 - 62         20 - 69
      BMRKR2
        LOW                 21             23              11
        MEDIUM              15             18              23
        HIGH                15             14              26
    U
      AGE
        n                   3              2               4
        Mean (sd)      31.67 (3.21)    31 (5.66)      35.25 (3.1)
        IQR                 3              4              3.25
        min - max        28 - 34        27 - 35         31 - 38
      BMRKR2
        LOW                 2              1               1
        MEDIUM              1              0               2
        HIGH                0              1               1
    UNDIFFERENTIATED
      AGE
        n                   1              0               2
        Mean (sd)        28 (NA)        NaN (NA)       45 (1.41)
        IQR                 0              NA              1
        min - max        28 - 28       Inf - -Inf       44 - 46
      BMRKR2
        LOW                 1              0               2
        MEDIUM              0              0               0
        HIGH                0              0               0

We will now subset `ADSL` to include only males and females in the analysis in order to reduces the number of rows in the table:

    ADSL_M_F <- filter(ADSL, SEX %in% c("M", "F"))

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX") %>%
      analyze(c("AGE", "BMRKR2"), s_summary) %>%
      build_table(ADSL_M_F)

                        A: Drug X      B: Placebo    C: Combination
                         (N=130)        (N=132)         (N=126)
    ---------------------------------------------------------------
    F
      AGE
        n                   79             77              66
        Mean (sd)      32.76 (6.09)   34.12 (7.06)    35.2 (7.43)
        IQR                 9              8              6.75
        min - max        21 - 47        23 - 58         21 - 64
      BMRKR2
        LOW                 26             21              26
        MEDIUM              21             38              17
        HIGH                32             18              23
    M
      AGE
        n                   51             55              60
        Mean (sd)      35.57 (7.08)   37.44 (8.69)    35.38 (8.24)
        IQR                 11             9               11
        min - max        23 - 50        21 - 62         20 - 69
      BMRKR2
        LOW                 21             23              11
        MEDIUM              15             18              23
        HIGH                15             14              26
    U
      AGE
        n                   0              0               0
        Mean (sd)        NaN (NA)       NaN (NA)        NaN (NA)
        IQR                 NA             NA              NA
        min - max       Inf - -Inf     Inf - -Inf      Inf - -Inf
      BMRKR2
        LOW                 0              0               0
        MEDIUM              0              0               0
        HIGH                0              0               0
    UNDIFFERENTIATED
      AGE
        n                   0              0               0
        Mean (sd)        NaN (NA)       NaN (NA)        NaN (NA)
        IQR                 NA             NA              NA
        min - max       Inf - -Inf     Inf - -Inf      Inf - -Inf
      BMRKR2
        LOW                 0              0               0
        MEDIUM              0              0               0
        HIGH                0              0               0

Note that the `UNDIFFERENTIATED` and `U` levels still show up in the table. This is because tabulation respects the factor levels and level order, exactly as the `split` and `table` function do. If empty levels should be dropped then `rtables` needs to know that at splitting time via the `split_fun` argument in `split_rows_by`. There are a number of predefined functions. For this example `drop_split_levels` is required to drop the empty levels at splitting time. Splitting is a big topic and will be eventually addressed in a specific package vignette.

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX", split_fun = drop_split_levels, child_labels = "visible") %>%
      analyze(c("AGE", "BMRKR2"), s_summary) %>%
      build_table(ADSL_M_F)

                     A: Drug X      B: Placebo    C: Combination
                      (N=130)        (N=132)         (N=126)
    ------------------------------------------------------------
    F
      AGE
        n                79             77              66
        Mean (sd)   32.76 (6.09)   34.12 (7.06)    35.2 (7.43)
        IQR              9              8              6.75
        min - max     21 - 47        23 - 58         21 - 64
      BMRKR2
        LOW              26             21              26
        MEDIUM           21             38              17
        HIGH             32             18              23
    M
      AGE
        n                51             55              60
        Mean (sd)   35.57 (7.08)   37.44 (8.69)    35.38 (8.24)
        IQR              11             9               11
        min - max     23 - 50        21 - 62         20 - 69
      BMRKR2
        LOW              21             23              11
        MEDIUM           15             18              23
        HIGH             15             14              26

In the table above the labels `M` and `F` are not very descriptive. You can add the full label as follows:

    ADSL_M_F_l <- ADSL_M_F %>%
      mutate(lbl_sex = case_when(
        SEX == "M" ~ "Male",
        SEX == "F" ~ "Female",
        SEX == "U" ~ "Unknown",
        SEX == "UNDIFFERENTIATED" ~ "Undifferentiated"
      ))

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "visible") %>%
      analyze(c("AGE", "BMRKR2"), s_summary) %>%
      build_table(ADSL_M_F_l)

                     A: Drug X      B: Placebo    C: Combination
                      (N=130)        (N=132)         (N=126)
    ------------------------------------------------------------
    Female
      AGE
        n                79             77              66
        Mean (sd)   32.76 (6.09)   34.12 (7.06)    35.2 (7.43)
        IQR              9              8              6.75
        min - max     21 - 47        23 - 58         21 - 64
      BMRKR2
        LOW              26             21              26
        MEDIUM           21             38              17
        HIGH             32             18              23
    Male
      AGE
        n                51             55              60
        Mean (sd)   35.57 (7.08)   37.44 (8.69)    35.38 (8.24)
        IQR              11             9               11
        min - max     23 - 50        21 - 62         20 - 69
      BMRKR2
        LOW              21             23              11
        MEDIUM           15             18              23
        HIGH             15             14              26

For the next table variation we only stratify by gender for the `AGE` analysis. To do this the `nested` argument has to be set to `FALSE` in `analyze` call:

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "visible") %>%
      analyze("AGE", s_summary, show_labels = "visible") %>%
      analyze("BMRKR2", s_summary, nested = FALSE,  show_labels = "visible") %>%
      build_table(ADSL_M_F_l)

                     A: Drug X      B: Placebo    C: Combination
                      (N=130)        (N=132)         (N=126)
    ------------------------------------------------------------
    Female
      AGE
        n                79             77              66
        Mean (sd)   32.76 (6.09)   34.12 (7.06)    35.2 (7.43)
        IQR              9              8              6.75
        min - max     21 - 47        23 - 58         21 - 64
    Male
      AGE
        n                51             55              60
        Mean (sd)   35.57 (7.08)   37.44 (8.69)    35.38 (8.24)
        IQR              11             9               11
        min - max     23 - 50        21 - 62         20 - 69
    BMRKR2
      LOW                47             44              37
      MEDIUM             36             56              40
      HIGH               47             32              49

Once we split the rows into groups (`Male` and `Female` here) one might want to summarize groups: usually by showing count and column percentages. This is especially important if we have missing data. For example if we create the above table but add missing data to the `AGE` variable:

    insert_NAs <- function(x) {
      x[sample(c(TRUE, FALSE), length(x), TRUE, prob = c(0.2, 0.8))] <- NA
      x
    }

    set.seed(1)
    ADSL_NA <- ADSL_M_F_l %>%
      mutate(AGE = insert_NAs(AGE))

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "visible") %>%
      analyze("AGE", s_summary) %>%
      analyze("BMRKR2", s_summary, nested = FALSE,  show_labels = "visible") %>%
      build_table(filter(ADSL_NA, SEX %in% c("M", "F")))

                   A: Drug X      B: Placebo    C: Combination
                    (N=130)        (N=132)         (N=126)
    ----------------------------------------------------------
    Female
      n                65             61              54
      Mean (sd)   32.71 (6.07)   34.33 (7.31)    34.61 (6.78)
      IQR              9              10             6.75
      min - max     21 - 47        23 - 58         21 - 54
    Male
      n                44             44              50
      Mean (sd)   35.66 (6.78)   36.93 (8.18)    35.64 (8.42)
      IQR             10.5           8.25           10.75
      min - max     24 - 48        21 - 58         20 - 69
    BMRKR2
      LOW              47             44              37
      MEDIUM           36             56              40
      HIGH             47             32              49

Here it is not easy to see how many females and males there are in each arm as `n` represents the number of non-missing data elements in the variables. Groups within rows that are defined by splitting can be summarized with `summarize_row_groups`, for example:

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels) %>%
      summarize_row_groups()  %>%
      analyze("AGE", s_summary) %>%
      analyze("BMRKR2", afun = s_summary, nested = FALSE,  show_labels = "visible") %>%
      build_table(filter(ADSL_NA, SEX %in% c("M", "F")))

                   A: Drug X      B: Placebo    C: Combination
                    (N=130)        (N=132)         (N=126)
    ----------------------------------------------------------
    Female         79 (60.8%)     77 (58.3%)      66 (52.4%)
      n                65             61              54
      Mean (sd)   32.71 (6.07)   34.33 (7.31)    34.61 (6.78)
      IQR              9              10             6.75
      min - max     21 - 47        23 - 58         21 - 54
    Male           51 (39.2%)     55 (41.7%)      60 (47.6%)
      n                44             44              50
      Mean (sd)   35.66 (6.78)   36.93 (8.18)    35.64 (8.42)
      IQR             10.5           8.25           10.75
      min - max     24 - 48        21 - 58         20 - 69
    BMRKR2
      LOW              47             44              37
      MEDIUM           36             56              40
      HIGH             47             32              49

There are a couple of things to note here.

- Group summaries produce “content” rows. Visually it’s impossible to distinguish data rows from content rows. Their difference is justified (and it’s an important design decision) because when we paginate tables the content rows are by default repeated if a group gets divided via pagination.
- Conceptually the content rows summarize the patient population which is analyzed and hence is often the count & group percentages (default behavior of `summarize_row_groups`).

We can recreate this default behavior (count percentage) by defining a `cfun` for illustrative purposes here as it results in the same table as above:

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      add_colcounts() %>%
      split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels) %>%
      summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) {
        in_rows(
          rcell(nrow(df) * c(1, 1/.N_col), format = "xx (xx.xx%)"),
          .labels = labelstr
        )
      })  %>%
      analyze("AGE", s_summary) %>%
      analyze("BEP01FL", afun = s_summary, nested = FALSE,  show_labels = "visible") %>%
      build_table(filter(ADSL_NA, SEX %in% c("M", "F")))

                   A: Drug X      B: Placebo    C: Combination
                    (N=130)        (N=132)         (N=126)
    ----------------------------------------------------------
    Female        79 (60.77%)    77 (58.33%)     66 (52.38%)
      n                65             61              54
      Mean (sd)   32.71 (6.07)   34.33 (7.31)    34.61 (6.78)
      IQR              9              10             6.75
      min - max     21 - 47        23 - 58         21 - 54
    Male          51 (39.23%)    55 (41.67%)     60 (47.62%)
      n                44             44              50
      Mean (sd)   35.66 (6.78)   36.93 (8.18)    35.64 (8.42)
      IQR             10.5           8.25           10.75
      min - max     24 - 48        21 - 58         20 - 69
    BEP01FL
      Y                67             63              65
      N                63             69              61

Note that `cfun` differs from `afun` (which is used in `analyze`) in that `cfun` does not operate on variables but rather on `data.frame`s or `tibble`s which are passed via the `df` argument (`afun` can optionally request `df` too). Further, `cfun` gives the default group label (factor level from splitting) as an argument to `labelstr` and hence it could be modified:

    basic_table() %>%
      split_cols_by(var = "ARM") %>%
      split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "hidden") %>%
      summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) {
        in_rows(
           rcell(nrow(df) * c(1, 1/.N_col), format = "xx (xx.xx%)"),
           .labels = paste0(labelstr, ": count (perc.)")
        )
      })  %>%
      analyze("AGE", s_summary) %>%
      analyze("BEP01FL", s_summary, nested = FALSE, show_labels = "visible") %>%
      build_table(filter(ADSL_NA, SEX %in% c("M", "F")))

                             A: Drug X      B: Placebo    C: Combination
    --------------------------------------------------------------------
    Female: count (perc.)   79 (60.77%)    77 (58.33%)     66 (52.38%)
      n                          65             61              54
      Mean (sd)             32.71 (6.07)   34.33 (7.31)    34.61 (6.78)
      IQR                        9              10             6.75
      min - max               21 - 47        23 - 58         21 - 54
    Male: count (perc.)     51 (39.23%)    55 (41.67%)     60 (47.62%)
      n                          44             44              50
      Mean (sd)             35.66 (6.78)   36.93 (8.18)    35.64 (8.42)
      IQR                       10.5           8.25           10.75
      min - max               24 - 48        21 - 58         20 - 69
    BEP01FL
      Y                          67             63              65
      N                          63             69              61

### Using Layouts

Layouts have a couple of advantages over tabulating the tables directly:

- the creation of layouts requires the analyst to describe the problem in an abstract way
  - i.e. they separate the analyses description from the actual data
- referencing variable names happens via strings (no non-standard evaluation (NSE) is needed, though this is arguably either feature or a short coming)
- layouts can be reused

Here is an example that demonstrates the reusability of layouts:

    lyt <- basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      analyze(c("AGE", "SEX"), afun = s_summary)

    lyt

    A Pre-data Table Layout

    Column-Split Structure:
    ARM (lvls)

    Row-Split Structure:
    ( (** multivar analysis **) -> AGE, SEX (** multivar analysis **) -> ) (** multivar analysis **)

We can now build a table for `ADSL`

    build_table(lyt, ADSL)

                          A: Drug X     B: Placebo    C: Combination
                           (N=134)        (N=134)        (N=132)
    ----------------------------------------------------------------
    AGE
      n                      134            134            132
      Mean (sd)          33.77 (6.55)   35.43 (7.9)    35.43 (7.72)
      IQR                     11            10              10
      min - max            21 - 50        21 - 62        20 - 69
    SEX
      F                       79            77              66
      M                       51            55              60
      U                       3              2              4
      UNDIFFERENTIATED        1              0              2

or for all patients that are older than 18:

    build_table(lyt, ADSL %>% filter(AGE > 18))

                          A: Drug X     B: Placebo    C: Combination
                           (N=134)        (N=134)        (N=132)
    ----------------------------------------------------------------
    AGE
      n                      134            134            132
      Mean (sd)          33.77 (6.55)   35.43 (7.9)    35.43 (7.72)
      IQR                     11            10              10
      min - max            21 - 50        21 - 62        20 - 69
    SEX
      F                       79            77              66
      M                       51            55              60
      U                       3              2              4
      UNDIFFERENTIATED        1              0              2

## Adverse Events

There are a number of different adverse event tables. We will now present two tables that show adverse events by id and then by grade and by id.

This time we won’t use the `ADAE` dataset from `random.cdisc.data` but rather generate a dataset on the fly (see Adrian’s 2016 Phuse paper):

    set.seed(1)

    lookup <- tribble(
      ~AEDECOD,                          ~AEBODSYS,                                         ~AETOXGR,
      'HEADACHE',                        "NERVOUS SYSTEM DISORDERS",                        "5",
      'BACK PAIN',                       "MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS", "2",
      'GINGIVAL BLEEDING',               "GASTROINTESTINAL DISORDERS",                      "1",
      'HYPOTENSION',                     "VASCULAR DISORDERS",                              "3",
      'FAECES SOFT',                     "GASTROINTESTINAL DISORDERS",                      "2",
      'ABDOMINAL DISCOMFORT',            "GASTROINTESTINAL DISORDERS",                      "1",
      'DIARRHEA',                        "GASTROINTESTINAL DISORDERS",                      "1",
      'ABDOMINAL FULLNESS DUE TO GAS',   "GASTROINTESTINAL DISORDERS",                      "1",
      'NAUSEA (INTERMITTENT)',           "GASTROINTESTINAL DISORDERS",                      "2",
      'WEAKNESS',                        "MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS", "3",
      'ORTHOSTATIC HYPOTENSION',         "VASCULAR DISORDERS",                              "4"
    )

    normalize <- function(x) x/sum(x)
    weightsA <- normalize(c(0.1, dlnorm(seq(0, 5, length.out = 25), meanlog = 3)))
    weightsB <- normalize(c(0.2, dlnorm(seq(0, 5, length.out = 25))))

    N_pop <- 300
    ADSL2 <- data.frame(
      USUBJID = seq(1, N_pop, by = 1),
      ARM = sample(c('ARM A', 'ARM B'), N_pop, TRUE),
      SEX = sample(c('F', 'M'), N_pop, TRUE),
      AGE = 20 + rbinom(N_pop, size=40, prob=0.7)
    )

    l.adae <- mapply(ADSL2$USUBJID, ADSL2$ARM, ADSL2$SEX, ADSL2$AGE, FUN = function(id, arm, sex, age) {
      n_ae <- sample(0:25, 1, prob = if (arm == "ARM A") weightsA else weightsB)
      i <- sample(1:nrow(lookup), size = n_ae, replace = TRUE, prob = c(6, rep(1, 10))/16)
      lookup[i, ] %>%
        mutate(
          AESEQ = seq_len(n()),
          USUBJID = id, ARM = arm, SEX = sex, AGE = age
        )
    }, SIMPLIFY = FALSE)

    ADAE2 <- do.call(rbind, l.adae)
    ADAE2 <- ADAE2 %>%
      mutate(
        ARM = factor(ARM, levels = c("ARM A", "ARM B")),
        AEDECOD = as.factor(AEDECOD),
        AEBODSYS = as.factor(AEBODSYS),
        AETOXGR = factor(AETOXGR, levels = as.character(1:5))
      ) %>%
      select(USUBJID, ARM, AGE, SEX, AESEQ, AEDECOD, AEBODSYS, AETOXGR)

    ADAE2

    # A tibble: 3,118 x 8
       USUBJID ARM     AGE SEX   AESEQ AEDECOD           AEBODSYS            AETOXGR
         <dbl> <fct> <dbl> <chr> <int> <fct>             <fct>               <fct>
     1       1 ARM A    45 F         1 NAUSEA (INTERMIT… GASTROINTESTINAL D… 2
     2       1 ARM A    45 F         2 HEADACHE          NERVOUS SYSTEM DIS… 5
     3       1 ARM A    45 F         3 HEADACHE          NERVOUS SYSTEM DIS… 5
     4       1 ARM A    45 F         4 HEADACHE          NERVOUS SYSTEM DIS… 5
     5       1 ARM A    45 F         5 HEADACHE          NERVOUS SYSTEM DIS… 5
     6       1 ARM A    45 F         6 HEADACHE          NERVOUS SYSTEM DIS… 5
     7       1 ARM A    45 F         7 HEADACHE          NERVOUS SYSTEM DIS… 5
     8       1 ARM A    45 F         8 HEADACHE          NERVOUS SYSTEM DIS… 5
     9       1 ARM A    45 F         9 HEADACHE          NERVOUS SYSTEM DIS… 5
    10       1 ARM A    45 F        10 FAECES SOFT       GASTROINTESTINAL D… 2
    # … with 3,108 more rows

### Adverse Events By ID

We start by defining an events summary function:

    s_events_patients <- function(x, labelstr, .N_col) {
      in_rows(
        "Total number of patients with at least one event" =
          rcell(length(unique(x)) * c(1, 1/.N_col), format = "xx (xx.xx%)"),

        "Total number of events" = rcell(length(x), format = "xx")
      )
    }

So, for a population of `5` patients where

- one patient has 2 AEs
- one patient has 1 AE
- three patients have no AEs

we would get the following summary:

    s_events_patients(x = c("id 1", "id 1", "id 2"), .N_col = 5)

    RowsVerticalSection (in_rows) object print method:
    ----------------------------
                                              row_name formatted_cell indent_mod
    1 Total number of patients with at least one event        2 (40%)          0
    2                           Total number of events              3          0
                                             row_label
    1 Total number of patients with at least one event
    2                           Total number of events

The `.N_col` argument is a special keyword argument which `build_table` passes the population size for each respective column. For a list of keyword arguments for the functions passed to `afun` in `analyze` refer to the documentation with `?analyze`.

We now use the `s_events_patients` summary function in a tabulation:

    basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      analyze("USUBJID", s_events_patients) %>%
      build_table(ADAE2)

                                                          ARM A         ARM B
                                                        (N=2060)       (N=1058)
    -----------------------------------------------------------------------------
    Total number of patients with at least one event   114 (5.53%)   150 (14.18%)
    Total number of events                                2060           1058

Note that the column `N`’s are wrong as by default they are set to the number of rows per group (i.e. number of AEs per arm here). This also affects the percentages. For this table we are interested in the number of patients per column/arm which is usually taken from `ADSL` (variable `ADSL2` here):

    N_per_arm <- table(ADSL2$ARM)
    N_per_arm

    ARM A ARM B
      146   154

Since this information is not “pre-data” it needs to go to the table creation function `build_table`:

    basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      analyze("USUBJID", s_events_patients) %>%
      build_table(ADAE2, col_counts = N_per_arm)

                                                          ARM A          ARM B
                                                         (N=146)        (N=154)
    -----------------------------------------------------------------------------
    Total number of patients with at least one event   114 (78.08%)   150 (97.4%)
    Total number of events                                 2060          1058

We next calculate this information per system organ class:

    l <- basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      analyze("USUBJID", s_events_patients) %>%
      split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE)  %>%
      summarize_row_groups("USUBJID", cfun = s_events_patients)
      build_table(l, ADAE2, col_counts = N_per_arm)

                                                            ARM A          ARM B
                                                           (N=146)        (N=154)
    --------------------------------------------------------------------------------
    Total number of patients with at least one event     114 (78.08%)   150 (97.4%)
    Total number of events                                   2060           1058
    GASTROINTESTINAL DISORDERS
      Total number of patients with at least one event   114 (78.08%)   130 (84.42%)
      Total number of events                                 760            374
    MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
      Total number of patients with at least one event   98 (67.12%)     81 (52.6%)
      Total number of events                                 273            142
    NERVOUS SYSTEM DISORDERS
      Total number of patients with at least one event   113 (77.4%)    133 (86.36%)
      Total number of events                                 787            420
    VASCULAR DISORDERS
      Total number of patients with at least one event    93 (63.7%)     75 (48.7%)
      Total number of events                                 240            122

We now have to the add a count table of `AEDECOD` for each `AEBODSYS`. The default `analyze` behavior for a factor is to create the count table per level (using `rtab_inner`):

    tbl1 <- basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1)  %>%
      summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
      analyze("AEDECOD", indent_mod = -1) %>%
      build_table(ADAE2, col_counts = N_per_arm)

    tbl1

                                                              ARM A          ARM B
                                                             (N=146)        (N=154)
    ----------------------------------------------------------------------------------
      GASTROINTESTINAL DISORDERS
        Total number of patients with at least one event   114 (78.08%)   130 (84.42%)
        Total number of events                                 760            374
        ABDOMINAL DISCOMFORT                                   113             65
        ABDOMINAL FULLNESS DUE TO GAS                          119             65
        BACK PAIN                                               0              0
        DIARRHEA                                               107             53
        FAECES SOFT                                            122             58
        GINGIVAL BLEEDING                                      147             71
        HEADACHE                                                0              0
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                  152             62
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                0              0
      MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
        Total number of patients with at least one event   98 (67.12%)     81 (52.6%)
        Total number of events                                 273            142
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                              135             75
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                                0              0
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                               138             67
      NERVOUS SYSTEM DISORDERS
        Total number of patients with at least one event   113 (77.4%)    133 (86.36%)
        Total number of events                                 787            420
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               0              0
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                               787            420
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                0              0
      VASCULAR DISORDERS
        Total number of patients with at least one event    93 (63.7%)     75 (48.7%)
        Total number of events                                 240            122
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               0              0
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                                0              0
        HYPOTENSION                                            104             58
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                136             64
        WEAKNESS                                                0              0

The `indent_mod` argument enables relative indenting changes if the tree structure of the table does not result in the desired indentation by default.

This table so far is however not the usual adverse event table as it counts the total number of events and not the number of subjects one or more events for a particular term. To get the correct table we need to write a custom analysis function:

    table_count_once_per_id <- function(df, termvar = "AEDECOD", idvar = "USUBJID") {

      x <- df[[termvar]]
      id <- df[[idvar]]

      counts <- table(x[!duplicated(id)])

      in_rows(
        .list = as.vector(counts),
        .labels = names(counts)
      )
    }

    table_count_once_per_id(ADAE2)

    RowsVerticalSection (in_rows) object print method:
    ----------------------------
                            row_name formatted_cell indent_mod
    1           ABDOMINAL DISCOMFORT             23          0
    2  ABDOMINAL FULLNESS DUE TO GAS             21          0
    3                      BACK PAIN             20          0
    4                       DIARRHEA              7          0
    5                    FAECES SOFT             11          0
    6              GINGIVAL BLEEDING             15          0
    7                       HEADACHE            100          0
    8                    HYPOTENSION             16          0
    9          NAUSEA (INTERMITTENT)             21          0
    10       ORTHOSTATIC HYPOTENSION             14          0
    11                      WEAKNESS             16          0
                           row_label
    1           ABDOMINAL DISCOMFORT
    2  ABDOMINAL FULLNESS DUE TO GAS
    3                      BACK PAIN
    4                       DIARRHEA
    5                    FAECES SOFT
    6              GINGIVAL BLEEDING
    7                       HEADACHE
    8                    HYPOTENSION
    9          NAUSEA (INTERMITTENT)
    10       ORTHOSTATIC HYPOTENSION
    11                      WEAKNESS

So the desired AE table is:

    basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1)  %>%
      summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
      analyze("AEDECOD", afun = table_count_once_per_id, show_labels = "hidden", indent_mod = -1) %>%
      build_table(ADAE2, col_counts = N_per_arm)

                                                              ARM A          ARM B
                                                             (N=146)        (N=154)
    ----------------------------------------------------------------------------------
      GASTROINTESTINAL DISORDERS
        Total number of patients with at least one event   114 (78.08%)   130 (84.42%)
        Total number of events                                 760            374
        ABDOMINAL DISCOMFORT                                    24             28
        ABDOMINAL FULLNESS DUE TO GAS                           18             26
        BACK PAIN                                               0              0
        DIARRHEA                                                17             17
        FAECES SOFT                                             17             14
        GINGIVAL BLEEDING                                       18             25
        HEADACHE                                                0              0
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   20             20
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                0              0
      MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
        Total number of patients with at least one event   98 (67.12%)     81 (52.6%)
        Total number of events                                 273            142
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               58             45
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                                0              0
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                40             36
      NERVOUS SYSTEM DISORDERS
        Total number of patients with at least one event   113 (77.4%)    133 (86.36%)
        Total number of events                                 787            420
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               0              0
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                               113            133
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                0              0
      VASCULAR DISORDERS
        Total number of patients with at least one event    93 (63.7%)     75 (48.7%)
        Total number of events                                 240            122
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               0              0
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                                0              0
        HYPOTENSION                                             44             31
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 49             44
        WEAKNESS                                                0              0

Note that we are missing the overall summary in the first two rows. This can be added with another `analyze` call and then setting `nested` to `FALSE` in the subsequent `summarize_row_groups` call:

    tbl <- basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      analyze("USUBJID", afun = s_events_patients) %>%
      split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE, indent_mod = 1)  %>%
      summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
      analyze("AEDECOD", table_count_once_per_id, show_labels = "hidden", indent_mod = -1) %>%
      build_table(ADAE2, col_counts = N_per_arm)

    tbl

                                                              ARM A          ARM B
                                                             (N=146)        (N=154)
    ----------------------------------------------------------------------------------
    Total number of patients with at least one event       114 (78.08%)   150 (97.4%)
    Total number of events                                     2060           1058
      GASTROINTESTINAL DISORDERS
        Total number of patients with at least one event   114 (78.08%)   130 (84.42%)
        Total number of events                                 760            374
        ABDOMINAL DISCOMFORT                                    24             28
        ABDOMINAL FULLNESS DUE TO GAS                           18             26
        BACK PAIN                                               0              0
        DIARRHEA                                                17             17
        FAECES SOFT                                             17             14
        GINGIVAL BLEEDING                                       18             25
        HEADACHE                                                0              0
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   20             20
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                0              0
      MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
        Total number of patients with at least one event   98 (67.12%)     81 (52.6%)
        Total number of events                                 273            142
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               58             45
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                                0              0
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                40             36
      NERVOUS SYSTEM DISORDERS
        Total number of patients with at least one event   113 (77.4%)    133 (86.36%)
        Total number of events                                 787            420
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               0              0
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                               113            133
        HYPOTENSION                                             0              0
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 0              0
        WEAKNESS                                                0              0
      VASCULAR DISORDERS
        Total number of patients with at least one event    93 (63.7%)     75 (48.7%)
        Total number of events                                 240            122
        ABDOMINAL DISCOMFORT                                    0              0
        ABDOMINAL FULLNESS DUE TO GAS                           0              0
        BACK PAIN                                               0              0
        DIARRHEA                                                0              0
        FAECES SOFT                                             0              0
        GINGIVAL BLEEDING                                       0              0
        HEADACHE                                                0              0
        HYPOTENSION                                             44             31
        NAUSEA (INTERMITTENT)                                   0              0
        ORTHOSTATIC HYPOTENSION                                 49             44
        WEAKNESS                                                0              0

Finally, if we wanted to prune the 0 counts row we can do that with the `trim_rows` function:

    trim_rows(tbl)

                                                              ARM A          ARM B
                                                             (N=146)        (N=154)
    ----------------------------------------------------------------------------------
    Total number of patients with at least one event       114 (78.08%)   150 (97.4%)
    Total number of events                                     2060           1058
      GASTROINTESTINAL DISORDERS
        Total number of patients with at least one event   114 (78.08%)   130 (84.42%)
        Total number of events                                 760            374
        ABDOMINAL DISCOMFORT                                    24             28
        ABDOMINAL FULLNESS DUE TO GAS                           18             26
        DIARRHEA                                                17             17
        FAECES SOFT                                             17             14
        GINGIVAL BLEEDING                                       18             25
        NAUSEA (INTERMITTENT)                                   20             20
      MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
        Total number of patients with at least one event   98 (67.12%)     81 (52.6%)
        Total number of events                                 273            142
        BACK PAIN                                               58             45
        WEAKNESS                                                40             36
      NERVOUS SYSTEM DISORDERS
        Total number of patients with at least one event   113 (77.4%)    133 (86.36%)
        Total number of events                                 787            420
        HEADACHE                                               113            133
      VASCULAR DISORDERS
        Total number of patients with at least one event    93 (63.7%)     75 (48.7%)
        Total number of events                                 240            122
        HYPOTENSION                                             44             31
        ORTHOSTATIC HYPOTENSION                                 49             44

Pruning is a larger topic with a separate `rtables` package vignette.

### Adverse Events By ID and By Grade

The adverse events table by ID and by grade shows how many patients had at least one adverse event per grade for different subsets of the data (e.g. defined by system organ class).

For this table we do not show the zero count grades. Note that we add the “overall” groups with a custom split function.

    table_count_grade_once_per_id <- function(df, labelstr = "", gradevar = "AETOXGR", idvar = "USUBJID", grade_levels = NULL) {

      id <- df[[idvar]]
      grade <- df[[gradevar]]

      if (!is.null(grade_levels)) {
        stopifnot(all(grade %in% grade_levels))
        grade <- factor(grade, levels = grade_levels)
      }

      id_sel <- !duplicated(id)

      in_rows(
          "--Any Grade--" = sum(id_sel),
          .list =  as.list(table(grade[id_sel]))
        )
    }

    table_count_grade_once_per_id(ex_adae, grade_levels = 1:5)

    RowsVerticalSection (in_rows) object print method:
    ----------------------------
           row_name formatted_cell indent_mod     row_label
    1 --Any Grade--            365          0 --Any Grade--
    2             1            131          0             1
    3             2             70          0             2
    4             3             74          0             3
    5             4             25          0             4
    6             5             65          0             5

All of the layouting concepts needed to create this table have already been introduced so far:

    basic_table() %>%
      split_cols_by("ARM") %>%
      add_colcounts() %>%
      analyze("AETOXGR",
              afun = table_count_grade_once_per_id,
              extra_args = list(grade_levels = 1:5),
              var_labels = "- Any adverse events -", show_labels = "visible") %>%
      split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE, indent_mod = 1) %>%
      summarize_row_groups(cfun = table_count_grade_once_per_id, format = "xx", indent_mod = 1) %>%
      split_rows_by("AEDECOD", child_labels = "visible", indent_mod = -2)  %>%
      analyze("AETOXGR",
              afun = table_count_grade_once_per_id,
              extra_args = list(grade_levels = 1:5), show_labels = "hidden") %>%
      build_table(ADAE2, col_counts = N_per_arm)

                                                         ARM A     ARM B
                                                        (N=146)   (N=154)
    ---------------------------------------------------------------------
    - Any adverse events -
      --Any Grade--                                       114       150
      1                                                   32        34
      2                                                   22        30
      3                                                   11        21
      4                                                    8         6
      5                                                   41        59
      GASTROINTESTINAL DISORDERS
            --Any Grade--                                 114       130
            1                                             77        96
            2                                             37        34
            3                                              0         0
            4                                              0         0
            5                                              0         0
        ABDOMINAL DISCOMFORT
          --Any Grade--                                   68        49
          1                                               68        49
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ABDOMINAL FULLNESS DUE TO GAS
          --Any Grade--                                   73        51
          1                                               73        51
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        BACK PAIN
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        DIARRHEA
          --Any Grade--                                   68        40
          1                                               68        40
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        FAECES SOFT
          --Any Grade--                                   76        44
          1                                                0         0
          2                                               76        44
          3                                                0         0
          4                                                0         0
          5                                                0         0
        GINGIVAL BLEEDING
          --Any Grade--                                   80        52
          1                                               80        52
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        HEADACHE
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        HYPOTENSION
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        NAUSEA (INTERMITTENT)
          --Any Grade--                                   83        50
          1                                                0         0
          2                                               83        50
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ORTHOSTATIC HYPOTENSION
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        WEAKNESS
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
      MUSCULOSKELETAL AND CONNECTIVE TISSUE DISORDERS
            --Any Grade--                                 98        81
            1                                              0         0
            2                                             58        45
            3                                             40        36
            4                                              0         0
            5                                              0         0
        ABDOMINAL DISCOMFORT
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ABDOMINAL FULLNESS DUE TO GAS
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        BACK PAIN
          --Any Grade--                                   79        62
          1                                                0         0
          2                                               79        62
          3                                                0         0
          4                                                0         0
          5                                                0         0
        DIARRHEA
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        FAECES SOFT
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        GINGIVAL BLEEDING
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        HEADACHE
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        HYPOTENSION
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        NAUSEA (INTERMITTENT)
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ORTHOSTATIC HYPOTENSION
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        WEAKNESS
          --Any Grade--                                   73        43
          1                                                0         0
          2                                                0         0
          3                                               73        43
          4                                                0         0
          5                                                0         0
      NERVOUS SYSTEM DISORDERS
            --Any Grade--                                 113       133
            1                                              0         0
            2                                              0         0
            3                                              0         0
            4                                              0         0
            5                                             113       133
        ABDOMINAL DISCOMFORT
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ABDOMINAL FULLNESS DUE TO GAS
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        BACK PAIN
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        DIARRHEA
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        FAECES SOFT
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        GINGIVAL BLEEDING
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        HEADACHE
          --Any Grade--                                   113       133
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                               113       133
        HYPOTENSION
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        NAUSEA (INTERMITTENT)
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ORTHOSTATIC HYPOTENSION
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        WEAKNESS
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
      VASCULAR DISORDERS
            --Any Grade--                                 93        75
            1                                              0         0
            2                                              0         0
            3                                             44        31
            4                                             49        44
            5                                              0         0
        ABDOMINAL DISCOMFORT
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ABDOMINAL FULLNESS DUE TO GAS
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        BACK PAIN
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        DIARRHEA
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        FAECES SOFT
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        GINGIVAL BLEEDING
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        HEADACHE
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        HYPOTENSION
          --Any Grade--                                   66        43
          1                                                0         0
          2                                                0         0
          3                                               66        43
          4                                                0         0
          5                                                0         0
        NAUSEA (INTERMITTENT)
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0
        ORTHOSTATIC HYPOTENSION
          --Any Grade--                                   70        54
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                               70        54
          5                                                0         0
        WEAKNESS
          --Any Grade--                                    0         0
          1                                                0         0
          2                                                0         0
          3                                                0         0
          4                                                0         0
          5                                                0         0

## Response Table

The response table that we will create here is composed of 3 parts:

1. Binary response table
2. Unstratified analysis comparison vs. control group
3. Multinomial response table

Let’s start with the first part which is fairly simple to derive:

    ADRS_BESRSPI <- ex_adrs %>%
      filter(PARAMCD == "BESRSPI") %>%
      mutate(
        rsp = factor(AVALC %in% c("CR", "PR"), levels = c(TRUE, FALSE), labels = c("Responders", "Non-Responders")),
        is_rsp = (rsp == "Responders")
      )

    s_proportion <- function(x, .N_col) {
       in_rows(.list = lapply(as.list(table(x)), function(xi) rcell(xi * c(1, 1/.N_col), format = "xx.xx (xx.xx%)")))
    }

    basic_table() %>%
      split_cols_by("ARMCD", ref_group = "ARM A") %>%
      add_colcounts() %>%
      analyze("rsp", s_proportion, show_labels = "hidden") %>%
      build_table(ADRS_BESRSPI)

                        ARM A          ARM B         ARM C
                       (N=134)        (N=134)       (N=132)
    ----------------------------------------------------------
    Responders       114 (85.07%)   90 (67.16%)   120 (90.91%)
    Non-Responders   20 (14.93%)    44 (32.84%)    12 (9.09%)

Note that we did set the `ref_group` argument in `split_cols_by` which for the current table had no effect as we only use the cell data for the responder and non-responder counting. The `ref_group` argument is needed for the part 2. and 3. of the table.

We will now look the implementation of part “2. Unstratified analysis comparison vs. control group.” Let’s start with the analysis function:

    s_unstratified_response_analysis <- function(x, .ref_group, .in_ref_col) {

      if (.in_ref_col) {
        return(in_rows(
            "Difference in Response Rates (%)" = rcell(numeric(0)),
            "95% CI (Wald, with correction)" = rcell(numeric(0)),
            "p-value (Chi-Squared Test)" = rcell(numeric(0)),
            "Odds Ratio (95% CI)" = rcell(numeric(0))
        ))
      }

      fit <- stats::prop.test(
        x = c(sum(x), sum(.ref_group)),
        n = c(length(x), length(.ref_group)),
        correct = FALSE
      )

      fit_glm <- stats::glm(
        formula = rsp ~ group,
        data = data.frame(
          rsp = c(.ref_group, x),
          group = factor(rep(c("ref", "x"), times = c(length(.ref_group), length(x))), levels = c("ref", "x"))
        ),
        family = binomial(link = "logit")
      )

      in_rows(
          "Difference in Response Rates (%)" = non_ref_rcell((mean(x) - mean(.ref_group)) * 100,
                                                             .in_ref_col, format = "xx.xx") ,
          "95% CI (Wald, with correction)" = non_ref_rcell(fit$conf.int * 100,
                                                           .in_ref_col, format = "(xx.xx, xx.xx)"),
          "p-value (Chi-Squared Test)" = non_ref_rcell(fit$p.value,
                                                       .in_ref_col, format = "x.xxxx | (<0.0001)"),
          "Odds Ratio (95% CI)" = non_ref_rcell(c(
              exp(stats::coef(fit_glm)[-1]),
              exp(stats::confint.default(fit_glm, level = .95)[-1, , drop = FALSE])
          ),
          .in_ref_col, format = "xx.xx (xx.xx - xx.xx)")
      )
    }

    s_unstratified_response_analysis(
      x = ADRS_BESRSPI %>% filter(ARM == "A: Drug X") %>% pull(is_rsp),
      .ref_group = ADRS_BESRSPI %>% filter(ARM == "B: Placebo") %>% pull(is_rsp),
      .in_ref_col = FALSE
    )

    RowsVerticalSection (in_rows) object print method:
    ----------------------------
                              row_name     formatted_cell indent_mod
    1 Difference in Response Rates (%)              17.91          0
    2   95% CI (Wald, with correction)      (7.93, 27.89)          0
    3       p-value (Chi-Squared Test)             0.0006          0
    4              Odds Ratio (95% CI) 2.79 (1.53 - 5.06)          0
                             row_label
    1 Difference in Response Rates (%)
    2   95% CI (Wald, with correction)
    3       p-value (Chi-Squared Test)
    4              Odds Ratio (95% CI)

Hence we can now add the next vignette to the table:

    basic_table() %>%
      split_cols_by("ARMCD", ref_group = "ARM A") %>%
      add_colcounts() %>%
      analyze("rsp", s_proportion, show_labels = "hidden") %>%
      analyze("is_rsp", s_unstratified_response_analysis, show_labels = "visible", var_labels = "Unstratified Response Analysis") %>%
      build_table(ADRS_BESRSPI)

                                            ARM A             ARM B               ARM C
                                           (N=134)           (N=134)             (N=132)
    ------------------------------------------------------------------------------------------
    Responders                           114 (85.07%)      90 (67.16%)         120 (90.91%)
    Non-Responders                       20 (14.93%)       44 (32.84%)          12 (9.09%)
    Unstratified Response Analysis
      Difference in Response Rates (%)                       -17.91                5.83
      95% CI (Wald, with correction)                     (-27.89, -7.93)      (-1.94, 13.61)
      p-value (Chi-Squared Test)                             0.0006               0.1436
      Odds Ratio (95% CI)                               0.36 (0.2 - 0.65)   1.75 (0.82 - 3.75)

Next we will add part 3. the “multinomial response table”. To do so, we are adding a row-split by response level, and then doing the same thing as we did for the binary response table above.

    s_prop <- function(df, .N_col) {
      in_rows(
        "95% CI (Wald, with correction)" = rcell(binom.test(nrow(df), .N_col)$conf.int * 100, format = "(xx.xx, xx.xx)")
      )
    }

    s_prop(
      df = ADRS_BESRSPI %>% filter(ARM == "A: Drug X", AVALC == "CR"),
      .N_col = sum(ADRS_BESRSPI$ARM == "A: Drug X")
    )

    RowsVerticalSection (in_rows) object print method:
    ----------------------------
                            row_name formatted_cell indent_mod
    1 95% CI (Wald, with correction) (49.38, 66.67)          0
                           row_label
    1 95% CI (Wald, with correction)

We can now create the final response table with all three parts:

    basic_table() %>%
      split_cols_by("ARMCD", ref_group = "ARM A") %>%
      add_colcounts() %>%
      analyze("rsp", s_proportion, show_labels = "hidden") %>%
      analyze("is_rsp", s_unstratified_response_analysis,
              show_labels = "visible", var_labels = "Unstratified Response Analysis") %>%
      split_rows_by(
        var = "AVALC",
        split_fun = reorder_split_levels(neworder = c("CR", "PR", "SD", "NON CR/PD", "PD", "NE"), drlevels = TRUE),
        nested = FALSE
      ) %>%
      summarize_row_groups() %>%
      analyze("AVALC", afun = s_prop) %>%
      build_table(ADRS_BESRSPI)

                                             ARM A              ARM B               ARM C
                                            (N=134)            (N=134)             (N=132)
    --------------------------------------------------------------------------------------------
    Responders                            114 (85.07%)       90 (67.16%)         120 (90.91%)
    Non-Responders                        20 (14.93%)        44 (32.84%)          12 (9.09%)
    Unstratified Response Analysis
      Difference in Response Rates (%)                         -17.91                5.83
      95% CI (Wald, with correction)                       (-27.89, -7.93)      (-1.94, 13.61)
      p-value (Chi-Squared Test)                               0.0006               0.1436
      Odds Ratio (95% CI)                                 0.36 (0.2 - 0.65)   1.75 (0.82 - 3.75)
    CR                                     78 (58.2%)         55 (41%)            97 (73.5%)
      95% CI (Wald, with correction)     (49.38, 66.67)    (32.63, 49.87)       (65.1, 80.79)
    PR                                     36 (26.9%)        35 (26.1%)           23 (17.4%)
      95% CI (Wald, with correction)     (19.58, 35.2)     (18.92, 34.41)       (11.38, 24.99)
    SD                                     20 (14.9%)        44 (32.8%)           12 (9.1%)
      95% CI (Wald, with correction)     (9.36, 22.11)     (24.97, 41.47)       (4.79, 15.34)

In case the we wanted to rename the levels of `AVALC` and remove the CI for `NE` we could do that as follows:

    rsp_label <- function(x) {
      rsp_full_label <- c(
        CR          = "Complete Response (CR)",
        PR          = "Partial Response (PR)",
        SD          = "Stable Disease (SD)",
        `NON CR/PD` = "Non-CR or Non-PD (NON CR/PD)",
        PD          = "Progressive Disease (PD)",
        NE          = "Not Evaluable (NE)",
        Missing     = "Missing",
        `NE/Missing` = "Missing or unevaluable"
      )
      stopifnot(all(x %in% names(rsp_full_label)))
      rsp_full_label[x]
    }

    tbl <- basic_table() %>%
      split_cols_by("ARMCD", ref_group = "ARM A") %>%
      add_colcounts() %>%
      analyze("rsp", s_proportion, show_labels = "hidden") %>%
      analyze("is_rsp", s_unstratified_response_analysis,
              show_labels = "visible", var_labels = "Unstratified Response Analysis") %>%
      split_rows_by(
        var = "AVALC",
        split_fun = keep_split_levels(c("CR", "PR", "SD", "PD"), reorder = TRUE),
        nested = FALSE
      ) %>%
      summarize_row_groups(cfun = function(df, labelstr, .N_col) {
        in_rows(nrow(df) * c(1, 1/.N_col), .formats = "xx (xx.xx%)", .labels = rsp_label(labelstr))
      }) %>%
      analyze("AVALC", afun = s_prop) %>%
      analyze("AVALC", afun = function(x, .N_col) {
        in_rows(rcell(sum(x == "NE") * c(1, 1/.N_col), format = "xx.xx (xx.xx%)"), .labels = rsp_label("NE"))
      }, nested = FALSE) %>%
      build_table(ADRS_BESRSPI)

    tbl

                                             ARM A              ARM B               ARM C
                                            (N=134)            (N=134)             (N=132)
    --------------------------------------------------------------------------------------------
    Responders                            114 (85.07%)       90 (67.16%)         120 (90.91%)
    Non-Responders                        20 (14.93%)        44 (32.84%)          12 (9.09%)
    Unstratified Response Analysis
      Difference in Response Rates (%)                         -17.91                5.83
      95% CI (Wald, with correction)                       (-27.89, -7.93)      (-1.94, 13.61)
      p-value (Chi-Squared Test)                               0.0006               0.1436
      Odds Ratio (95% CI)                                 0.36 (0.2 - 0.65)   1.75 (0.82 - 3.75)
    Complete Response (CR)                78 (58.21%)        55 (41.04%)         97 (73.48%)
      95% CI (Wald, with correction)     (49.38, 66.67)    (32.63, 49.87)       (65.1, 80.79)
    Partial Response (PR)                 36 (26.87%)        35 (26.12%)         23 (17.42%)
      95% CI (Wald, with correction)     (19.58, 35.2)     (18.92, 34.41)       (11.38, 24.99)
    Stable Disease (SD)                   20 (14.93%)        44 (32.84%)          12 (9.09%)
      95% CI (Wald, with correction)     (9.36, 22.11)     (24.97, 41.47)       (4.79, 15.34)
    Progressive Disease (PD)                 0 (0%)            0 (0%)               0 (0%)
      95% CI (Wald, with correction)       (0, 2.72)          (0, 2.72)           (0, 2.76)
    Not Evaluable (NE)                       0 (0%)            0 (0%)               0 (0%)

Note that the table is missing the rows gaps to make it more readable. The row spacing feature is on the `rtables` roadmap and will be implemented in future.