Chapter 4 Commonly Used Tables

4.1 Demographic Tables

4.1.1 rtables

Using rtables only:

Code
resetSession()
library(rtables)

a_demo_num <- function(x) {
    in_rows(n = length(x),
            "Mean (SD)" = rcell(c(mean(x, na.rm = TRUE),
                                  sd(x, na.rm=TRUE)), format = "xx.x (xx.x)"),
            "Median" = median(x,na.rm = TRUE),
            "Min - Max" = rcell(range(x, na.rm = TRUE), format = "xx.x - xx.x"))
}

a_demo_fac <- function(x) {
    in_rows(.list = c(c(n = length(x)), table(x)))
}

lyt <- basic_table(title = "x.x: Study Subject Data",
                   subtitles= c("x.x.x: Demographic Characteristics",
                                "Table x.x.x.x: Demographic Characteristics - Full Analysis Set"),
                   prov_footer = "Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY") |>
  split_cols_by("ARM") |>
  analyze(c("AGE", "SEX", "COUNTRY"), afun = list(AGE = a_demo_num, SEX = a_demo_fac,
                                                  COUNTRY = a_demo_fac))
 
build_table(lyt, ex_adsl)
x.x: Study Subject Data
x.x.x: Demographic Characteristics
Table x.x.x.x: Demographic Characteristics - Full Analysis Set

———————————————————————————————————————————————————————————————
                      A: Drug X    B: Placebo    C: Combination
———————————————————————————————————————————————————————————————
AGE                                                            
  n                      134           134            132      
  Mean (SD)          33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  
  Median                 33            35              35      
  Min - Max          21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  
SEX                                                            
  n                      134           134            132      
  F                      79            77              66      
  M                      51            55              60      
  U                       3             2              4       
  UNDIFFERENTIATED        1             0              2       
COUNTRY                                                        
  n                      134           134            132      
  CHN                    74            81              64      
  USA                    10            13              17      
  BRA                    13             7              10      
  PAK                    12             9              10      
  NGA                     8             7              11      
  RUS                     5             8              6       
  JPN                     5             4              9       
  GBR                     4             3              2       
  CAN                     3             2              3       
  CHE                     0             0              0       
———————————————————————————————————————————————————————————————

Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY

4.1.2 tern (+ rtables)

Code
resetSession()
library(tern)
lyt <- basic_table(title = "x.x: Study Subject Data",
                   subtitles= c("x.x.x: Demographic Characteristics",
                                "Table x.x.x.x: Demographic Characteristics - Full Analysis Set"),
                   prov_footer = "Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY") |>
  split_cols_by("ARM") |>
  analyze_vars(c("AGE", "SEX", "COUNTRY"))

build_table(lyt, ex_adsl)
x.x: Study Subject Data
x.x.x: Demographic Characteristics
Table x.x.x.x: Demographic Characteristics - Full Analysis Set

———————————————————————————————————————————————————————————————
                      A: Drug X    B: Placebo    C: Combination
———————————————————————————————————————————————————————————————
AGE                                                            
  n                      134           134            132      
  Mean (SD)          33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  
  Median                33.0          35.0            35.0     
  Min - Max          21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  
SEX                                                            
  n                      134           134            132      
  F                   79 (59%)     77 (57.5%)       66 (50%)   
  M                  51 (38.1%)     55 (41%)       60 (45.5%)  
  U                   3 (2.2%)      2 (1.5%)         4 (3%)    
  UNDIFFERENTIATED    1 (0.7%)          0           2 (1.5%)   
COUNTRY                                                        
  n                      134           134            132      
  CHN                74 (55.2%)    81 (60.4%)      64 (48.5%)  
  USA                 10 (7.5%)     13 (9.7%)      17 (12.9%)  
  BRA                 13 (9.7%)     7 (5.2%)       10 (7.6%)   
  PAK                  12 (9%)      9 (6.7%)       10 (7.6%)   
  NGA                  8 (6%)       7 (5.2%)       11 (8.3%)   
  RUS                 5 (3.7%)       8 (6%)         6 (4.5%)   
  JPN                 5 (3.7%)       4 (3%)         9 (6.8%)   
  GBR                  4 (3%)       3 (2.2%)        2 (1.5%)   
  CAN                 3 (2.2%)      2 (1.5%)        3 (2.3%)   
  CHE                     0             0              0       
———————————————————————————————————————————————————————————————

Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY

4.1.3 gt

Code
resetSession()
library(gt)
library(tidyverse)

# We will use ex_adsl but will assign a unit to the Age column

ex_adsl <- formatters::ex_adsl
gt_adsl <- ex_adsl
attr(gt_adsl$AGE, "units") <- "Years"

# This is a customized summary function
# It creates numeric and categorical summaries for specified variables, following the rtables exmaple

custom_summary <- function(df, group_var, sum_var){
  group_var <- rlang::ensym(group_var)
  sum_var <- rlang::ensym(sum_var)
  
  is_categorical <- is.character(eval(expr(`$`(df, !!sum_var)))) | is.factor(eval(expr(`$`(df, !!sum_var)))) 
  
  if (is_categorical){
    df <- df  |>  
      dplyr::group_by(!!group_var) |> 
      dplyr::mutate(N = n()) |> 
      dplyr::ungroup() |> 
      dplyr::group_by(!!group_var, !!sum_var) |> 
      dplyr::summarize(
        val = n(),
        sd = 100*n()/mean(N),
        .groups = "drop"
        ) |>
      tidyr::pivot_wider(id_cols = !!sum_var, names_from = !!group_var, values_from = c(val, sd)) |> 
      dplyr::rename(label = !!sum_var) |> 
      dplyr::mutate(isnum = FALSE,
                    across(where(is.numeric), ~ifelse(is.na(.), 0, .))) 
    
    sum_unit <- ", n (%)"
    
  } else {
    
    sum_unit <- sprintf(" (%s)", attr(eval(expr(`$`(df, !!sum_var))), "units"))
    
    df <- df |> 
      dplyr::group_by(!!group_var) |> 
      dplyr::summarize(
        n = sum(!is.na(!!sum_var)),
        mean = mean(!!sum_var, na.rm = TRUE),
        sd = sd(!!sum_var, na.rm = TRUE),
        median = median(!!sum_var, na.rm = TRUE),
        min = min(!!sum_var, na.rm = TRUE),
        max = max(!!sum_var, na.rm = TRUE),
        min_max = NA,
        .groups = "drop"
        ) |> 
      tidyr::pivot_longer(cols = c(n, mean, median, min_max), names_to = "label", values_to = "val") |> 
      dplyr::mutate(sd = ifelse(label == "mean", sd, NA),
                max = ifelse(label == "min_max", max, NA),
                min = ifelse(label == "min_max", min, NA),
                label = dplyr::recode(label, "mean" = "Mean (SD)", "min_max" = "Min - Max", "median" = "Median")) |>  
      tidyr::pivot_wider(id_cols = label, names_from = !!group_var, values_from = c(val, sd, min, max)) |> 
      dplyr::mutate(isnum = TRUE)
    
  }
  
  df |> 
    dplyr::mutate(category = paste0(stringr::str_to_title(deparse(substitute(!!sum_var))),
                                     sum_unit)) 
}

# Perform aggregation for variables Age, Sex and Country

adsl_summary <- purrr::map_df(.x = vars(AGE, SEX, COUNTRY),
                             .f = ~custom_summary(df = gt_adsl, group_var = ARM, sum_var = !!.x)) 

# Count number of patients per Arm

adsl_n <- ex_adsl |>
  dplyr::summarize(
    NLBL = sprintf("%s  \n(N=%i)",unique(ARM), dplyr::n()), 
    .by = ARM
    )

header_n <- as.list(adsl_n$NLBL)
names(header_n) <- paste0("val_", adsl_n$ARM)

# gt

gt(adsl_summary, 
   rowname_col = "label",
   groupname_col = "category") |>
  tab_header(
    title = "x.x: Study Subject Data",
    subtitle = md("x.x.x: Demographic Characteristics  \n  Table x.x.x.x: Demographic Characteristics - Full Analysis Set"),
    preheader = c("Protocol: XXXXX", "Cutoff date: DDMMYYYY")
  ) |> 
  tab_source_note("Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY") |> 
  opt_align_table_header(align = "left") |> 
  fmt_integer(columns = starts_with(c("val", "min", "max")), rows = label != "Mean (SD)") |> 
  fmt_number(columns = starts_with(c("val", "sd")), rows = label == "Mean (SD)", decimals = 1) |> 
  fmt_number(columns = starts_with("sd"), rows = isnum == FALSE, decimals = 1) |> 
  sub_missing(missing_text = "") |> 
  summary_rows(
    groups = c("Sex, n (%)", "Country, n (%)"),
    columns = starts_with("val"),
    fns = list(n = ~sum(.)),
    missing_text = "",
    side = "top"
    ) |> 
  cols_merge_n_pct(col_n = "val_A: Drug X", col_pct = "sd_A: Drug X") |>
  cols_merge_n_pct(col_n = "val_B: Placebo", col_pct = "sd_B: Placebo") |> 
  cols_merge_n_pct(col_n = "val_C: Combination", col_pct = "sd_C: Combination") |> 
  cols_merge_range(col_begin = "min_A: Drug X", col_end = "max_A: Drug X", sep = " - ") |> 
  cols_merge_range(col_begin = "min_B: Placebo", col_end = "max_B: Placebo", sep = " - ") |> 
  cols_merge_range(col_begin = "min_C: Combination", col_end = "max_C: Combination", sep = " - ") |> 
  cols_merge(columns = c("val_A: Drug X", "min_A: Drug X"), pattern = "{1}{2}") |> 
  cols_merge(columns = c("val_B: Placebo", "min_B: Placebo"), pattern = "{1}{2}") |> 
  cols_merge(columns = c("val_C: Combination", "min_C: Combination"), pattern = "{1}{2}") |> 
  cols_hide(columns = isnum) |> 
  cols_align(
    align = "center",
    columns = c("val_A: Drug X", "val_B: Placebo", "val_C: Combination")
    ) |> 
  cols_align(
    align = "left",
    columns = 1
    ) |> 
  tab_style(
    style = cell_text(indent = px(10)), 
    locations = cells_stub()
    ) |> 
  cols_label(
    .list = header_n,
    .fn = md
    ) |> 
  tab_options(
    table.font.size = 9,
    page.orientation = "landscape",
    page.numbering = TRUE,
    page.header.use_tbl_headings = TRUE,
    page.footer.use_tbl_notes = TRUE)
x.x: Study Subject Data
x.x.x: Demographic Characteristics
Table x.x.x.x: Demographic Characteristics - Full Analysis Set
A: Drug X
(N=134)
B: Placebo
(N=134)
C: Combination
(N=132)
Age (Years)
n 134
134
132
Mean (SD) 33.8 (6.6)
35.4 (7.9)
35.4 (7.7)
Median 33
35
35
Min - Max
21 - 50

21 - 62

20 - 69
Sex, n (%)
n 134 134 132
F 79 (59.0)
77 (57.5)
66 (50.0)
M 51 (38.1)
55 (41.0)
60 (45.5)
U 3 (2.2)
2 (1.5)
4 (3.0)
UNDIFFERENTIATED 1 (0.7)
0
2 (1.5)
Country, n (%)
n 134 134 132
CHN 74 (55.2)
81 (60.4)
64 (48.5)
USA 10 (7.5)
13 (9.7)
17 (12.9)
BRA 13 (9.7)
7 (5.2)
10 (7.6)
PAK 12 (9.0)
9 (6.7)
10 (7.6)
NGA 8 (6.0)
7 (5.2)
11 (8.3)
RUS 5 (3.7)
8 (6.0)
6 (4.5)
JPN 5 (3.7)
4 (3.0)
9 (6.8)
GBR 4 (3.0)
3 (2.2)
2 (1.5)
CAN 3 (2.2)
2 (1.5)
3 (2.3)
Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY

4.1.4 flextable

Code
# The two steps in creating 'Demographic Tables' are:
# 
# - summarize the information with the `flextable::summarizor()` function. 
# It computes a set of statistics for each variable by groups. It returns 
# a data.frame ready to be used by `flextable::as_flextable()`.
# - Create the flextable with the `as_flextable()` function.

resetSession()
ex_adsl <- formatters::ex_adsl

library(flextable)
library(tidyverse)
library(officer)

set_flextable_defaults(
  border.color = "#AAAAAA", font.family = "Open Sans",
font.size = 10, padding = 3, line_spacing = 1.4
)

# data
adsl <- select(ex_adsl, AGE, SEX, COUNTRY, ARM)

# In the illustration, we use labels from the column attributes.  

col_labels <- map_chr(adsl, function(x) attr(x, "label"))

# Now let's use the labels and customize the ‘flextable’ output.

ft <- summarizor(adsl, by = "ARM") |>
  as_flextable(sep_w = 0, separate_with = "variable", 
               spread_first_col = TRUE) |>
  align(i = ~ !is.na(variable), align = "left") |> 
  prepend_chunks(i = ~ is.na(variable), j  ="stat", as_chunk("\t") ) |> 
  labelizor(j = c("stat"), 
            labels = col_labels, part = "all") |> 
  autofit() |>
  add_header_lines(
    c("x.x: Study Subject Data",
      "x.x.x: Demographic Characteristics",
      "Table x.x.x.x: Demographic Characteristics - Full Analysis Set")) |> 
  add_footer_lines("Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY")

ft

x.x: Study Subject Data

x.x.x: Demographic Characteristics

Table x.x.x.x: Demographic Characteristics - Full Analysis Set

A: Drug X
(N=134)

B: Placebo
(N=134)

C: Combination
(N=132)

Age

Mean (SD)

33.8 (6.6)

35.4 (7.9)

35.4 (7.7)

Median (IQR)

33.0 (11.0)

35.0 (10.0)

35.0 (10.0)

Range

21.0 - 50.0

21.0 - 62.0

20.0 - 69.0

Sex

F

79 (59.0%)

77 (57.5%)

66 (50.0%)

M

51 (38.1%)

55 (41.0%)

60 (45.5%)

U

3 (2.2%)

2 (1.5%)

4 (3.0%)

UNDIFFERENTIATED

1 (0.7%)

0 (0.0%)

2 (1.5%)

Country

CHN

74 (55.2%)

81 (60.4%)

64 (48.5%)

USA

10 (7.5%)

13 (9.7%)

17 (12.9%)

BRA

13 (9.7%)

7 (5.2%)

10 (7.6%)

PAK

12 (9.0%)

9 (6.7%)

10 (7.6%)

NGA

8 (6.0%)

7 (5.2%)

11 (8.3%)

RUS

5 (3.7%)

8 (6.0%)

6 (4.5%)

JPN

5 (3.7%)

4 (3.0%)

9 (6.8%)

GBR

4 (3.0%)

3 (2.2%)

2 (1.5%)

CAN

3 (2.2%)

2 (1.5%)

3 (2.3%)

CHE

0 (0.0%)

0 (0.0%)

0 (0.0%)

Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY

4.1.5 tables

The tables package uses a different style than the other packages for tables such as this, where there are separate sections for age, sex and country breakdowns of the data. Rather than putting the section heading on a separate line, it normally puts the heading in a separate column to the left of the other columns.

Code
resetSession()

ex_adsl <- formatters::ex_adsl

library(tables)
table_options(doCSS = TRUE)

meansd <- function(x) sprintf("%.1f (%.1f)", mean(x), sd(x))

iqr <- function(x) quantile(x, 0.75) - quantile(x, 0.25)

medianiqr <- function(x) sprintf("%.1f (%.1f)", median(x), iqr(x))

minmax <- function(x) sprintf("%.1f - %.1f", min(x), max(x))

countpercent <- function(num, denom) 
  sprintf("%d (%.1f%%)", 
          length(num), 
          100*length(num)/length(denom))

count <- function(x) sprintf("(N=%d)", length(x))

tab <- tabular( Heading()*1*Heading()*count +
         Heading("Age (Years)")*
           AGE * (Heading("Mean (SD)")*meansd +
                  Heading("Median (IQR)")*medianiqr +
                  Heading("Min - Max")*minmax) +
         (Heading("Sex, n, (%)")*SEX +
          Heading("Country, n, (%)")*COUNTRY)*
            Heading()*Percent(denom = Equal(ARM), fn = countpercent) ~ 
         Heading()*ARM, 
         data = ex_adsl )
Warning in cbind(padNA, leftjustification): number of rows of result is not a
multiple of vector length (arg 1)
Code
useGroupLabels(tab, indent = "&emsp;")
A: Drug X B: Placebo C: Combination
(N=134) (N=134) (N=132)
Age (Years)      
 Mean (SD) 33.8 (6.6) 35.4 (7.9) 35.4 (7.7)
 Median (IQR) 33.0 (11.0) 35.0 (10.0) 35.0 (10.0)
 Min - Max 21.0 - 50.0 21.0 - 62.0 20.0 - 69.0
Sex, n, (%)      
 F 79 (59.0%) 77 (57.5%) 66 (50.0%)
 M 51 (38.1%) 55 (41.0%) 60 (45.5%)
 U 3 (2.2%) 2 (1.5%) 4 (3.0%)
 UNDIFFERENTIATED 1 (0.7%) 0 (0.0%) 2 (1.5%)
Country, n, (%)      
 CHN 74 (55.2%) 81 (60.4%) 64 (48.5%)
 USA 10 (7.5%) 13 (9.7%) 17 (12.9%)
 BRA 13 (9.7%) 7 (5.2%) 10 (7.6%)
 PAK 12 (9.0%) 9 (6.7%) 10 (7.6%)
 NGA 8 (6.0%) 7 (5.2%) 11 (8.3%)
 RUS 5 (3.7%) 8 (6.0%) 6 (4.5%)
 JPN 5 (3.7%) 4 (3.0%) 9 (6.8%)
 GBR 4 (3.0%) 3 (2.2%) 2 (1.5%)
 CAN 3 (2.2%) 2 (1.5%) 3 (2.3%)
 CHE 0 (0.0%) 0 (0.0%) 0 (0.0%)

4.1.6 tidytlg

Code
resetSession()
library(dplyr)
library(tidytlg)

adsl <- formatters::ex_adsl

# create univariate stats for age
tbl1 <- univar(adsl,
               rowvar = "AGE",
               colvar = "ARM",
               statlist = statlist(c("N","MEANSD","MEDIAN","RANGE")),
               row_header = "Age (years)",
               decimal = 0)

# create counts (percentages) for gender categories
tbl2 <- freq(adsl,
             rowvar = "SEX",
             colvar = "ARM",
             statlist = statlist(c("N", "n (x.x%)")),
             row_header = "Gender, n(%)")

# create counts (percentages) for country
tbl3 <- freq(adsl,
             rowvar = "COUNTRY",
             colvar = "ARM",
             statlist = statlist(c("N", "n (x.x%)")),
             row_header = "Country, n(%)",
             descending_by = "C: Combination")

# combine analysis results together
tbl <- bind_table(tbl1, tbl2, tbl3)

# output the analysis results
gentlg(huxme       = tbl,
       format      = "HTML",
       print.hux = FALSE,
       file        = "Table x.x.x.x",
       orientation = "portrait",
       title = "Demographic Characteristics - Full Analysis Set",
       footers = "Source: ADSL DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY",
       colheader = c("","A: Drug X","B: Placebo","C: Combination"))
[[1]]
  <div style='border-top :1pt solid; border-bottom :1pt solid; '>              
  <div style = "text-indent: -36px; padding-left: 36px;"> Table                
  x.x.x.x: &emsp; Demographic Characteristics - Full Analysis                  
  Set</div>                                                                    
                                       <div           <div           <div      
                                   style='borde   style='borde   style='borde  
                                   r-bottom:1pt   r-bottom:1pt   r-bottom:1pt  
                                    solid'> A:     solid'> B:     solid'> C:   
                                      Drug X        Placebo      Combination   
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'> Age (years)                                                        
  <div style='text-indent:             134            134            132       
  -17.76px; padding-left:                                                      
  35.52px'> N                                                                  
  <div style='text-indent:         33.8 (6.55)    35.4 (7.90)    35.4 (7.72)   
  -17.76px; padding-left:                                                      
  53.28px'> Mean (SD)                                                          
  <div style='text-indent:             33.0           35.0           35.0      
  -17.76px; padding-left:                                                      
  53.28px'> Median                                                             
  <div style='text-indent:           (21; 50)       (21; 62)       (20; 69)    
  -17.76px; padding-left:                                                      
  53.28px'> Range                                                              
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'> Gender, n(%)                                                       
  <div style='text-indent:             134            134            132       
  -17.76px; padding-left:                                                      
  35.52px'> N                                                                  
  <div style='text-indent:          79 (59.0%)     77 (57.5%)     66 (50.0%)   
  -17.76px; padding-left:                                                      
  53.28px'> F                                                                  
  <div style='text-indent:          51 (38.1%)     55 (41.0%)     60 (45.5%)   
  -17.76px; padding-left:                                                      
  53.28px'> M                                                                  
  <div style='text-indent:           3 (2.2%)       2 (1.5%)       4 (3.0%)    
  -17.76px; padding-left:                                                      
  53.28px'> U                                                                  
  <div style='text-indent:           1 (0.7%)          0           2 (1.5%)    
  -17.76px; padding-left:                                                      
  53.28px'> UNDIFFERENTIATED                                                   
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'> Country, n(%)                                                      
  <div style='text-indent:             134            134            132       
  -17.76px; padding-left:                                                      
  35.52px'> N                                                                  
  <div style='text-indent:          74 (55.2%)     81 (60.4%)     64 (48.5%)   
  -17.76px; padding-left:                                                      
  53.28px'> CHN                                                                
  <div style='text-indent:          10 (7.5%)      13 (9.7%)      17 (12.9%)   
  -17.76px; padding-left:                                                      
  53.28px'> USA                                                                
  <div style='text-indent:           8 (6.0%)       7 (5.2%)      11 (8.3%)    
  -17.76px; padding-left:                                                      
  53.28px'> NGA                                                                
  <div style='text-indent:          13 (9.7%)       7 (5.2%)      10 (7.6%)    
  -17.76px; padding-left:                                                      
  53.28px'> BRA                                                                
  <div style='text-indent:          12 (9.0%)       9 (6.7%)      10 (7.6%)    
  -17.76px; padding-left:                                                      
  53.28px'> PAK                                                                
  <div style='text-indent:           5 (3.7%)       4 (3.0%)       9 (6.8%)    
  -17.76px; padding-left:                                                      
  53.28px'> JPN                                                                
  <div style='text-indent:           5 (3.7%)       8 (6.0%)       6 (4.5%)    
  -17.76px; padding-left:                                                      
  53.28px'> RUS                                                                
  <div style='text-indent:           3 (2.2%)       2 (1.5%)       3 (2.3%)    
  -17.76px; padding-left:                                                      
  53.28px'> CAN                                                                
  <div style='text-indent:           4 (3.0%)       3 (2.2%)       2 (1.5%)    
  -17.76px; padding-left:                                                      
  53.28px'> GBR                                                                
  <div style='text-indent:              0              0              0        
  -17.76px; padding-left:                                                      
  53.28px'> CHE                                                                
  <div style='border-top:1pt solid;'> <br />Source: ADSL DDMMYYYY              
  hh:mm; Listing x.xx; SDTM package: DDMMYYYY                                  
                        <div style='border-bottom:1pt solid'> [table           
  x.x.x.x.html][/home/runner/work/_temp/5a83a2a5-6d01-4ccf-8f57-0c47           
                                          2be60bd2] 28JUN2024, 18:06           

Column names: label, A: Drug X, B: Placebo, C: Combination

4.1.7 tfrmt

Please note that the tfrmt package is intended for use with mock data or ARD (analysis results data). This package creates the same tables as other packages but requires the starting data to be transformed first.

The first chunk of code takes the CDISC data and modifies it into an ARD. The second chunk demonstrates how to format the table.

Code
resetSession()
library(tidyverse)
library(tfrmt)

# Get data
data("cadsl", package = "random.cdisc.data")

# Number of unique subjects per ARM
big_n <- cadsl |>
  dplyr::group_by(ARM) |>
  dplyr::summarize(
    N = dplyr::n_distinct(USUBJID)
  )

# Join big_n with adsl
adsl_with_n <- cadsl |>
  dplyr::left_join(big_n, by = "ARM")

# Explore column: AGE
age_stats <-
  adsl_with_n |>
  group_by(ARM) |>
  reframe(
    n = n_distinct(USUBJID),
    Mean = mean(AGE),
    SD = sd(AGE),
    Median = median(AGE),
    Min = min(AGE),
    Max = max(AGE)
  ) |>
  pivot_longer(
    c("n", "Mean", "SD", "Median", "Min", "Max")
  ) |>
  mutate(
    group = "Age (years)",
    label = case_when(name == "Mean" ~ "Mean (SD)",
                      name == "SD" ~ "Mean (SD)",
                      name == "Min" ~ "Min - Max",
                      name == "Max" ~ "Min - Max",
                      TRUE ~ name)
  )

sex_n <-
  adsl_with_n |>
  group_by(ARM, SEX) |>
  reframe(
    n = n(),
    pct = (n/N)*100
  ) |>
  distinct() |>
  pivot_longer(
    c("n", "pct")
  ) |>
  rename(
    label = SEX
  ) |>
  mutate(
    group = "Sex"
  )

# Explore column: COUNTRY
country_n <-
  adsl_with_n |>
  group_by(ARM, COUNTRY) |>
  reframe(
    n = n(),
    pct = (n/N)*100
) |>
  distinct() |>
  pivot_longer(
    c("n", "pct")
  ) |>
  rename(
    label = COUNTRY
  ) |>
  mutate(
    group = "Country"
  )

# Header n
header_n <- big_n |>
  dplyr::rename(value = N) |>
  dplyr::mutate(name = "header_n")

# Create ARD
demog_ard <-
  bind_rows(
    age_stats,
    sex_n,
    country_n,
    #header_n
  ) |>
  rename(
    column = ARM,
    param = name
  ) |>
  select(
    group, label, param, column, value
  ) |>
  group_by(group, label)

Now we can used the demog_ard to make the demographic table using tfrmt.

Code
tfrmt(
  # Add titles
  title = "x.x: Study Subject Data",
  subtitle = c("x.x.x: Demographic Characteristics. \n
               Table x.x.x.x: Demographic Characteristics - Full Analysis Set"),

  # Specify table features
  group = group,
  label = label,
  column = column,
  param = param,
  value = value,

  # Define cell formatting
  body_plan = body_plan(
    # Define rounding and structure of values in each row
    frmt_structure(group_val = ".default", label_val = ".default", frmt("xx")),

    frmt_structure(group_val = "Age (years)",
                   label_val = c("Mean (SD)"),
                   frmt_combine(
                     "{Mean} ({SD})",
                     Mean = frmt("xx.x"),
                     SD = frmt("x.x") )),

    frmt_structure(group_val = "Age (years)",
                   label_val = c("Min - Max"),
                   frmt_combine(
                     "{Min} - {Max}",
                     frmt("xx.x") )),

    frmt_structure(group_val = "Sex",
                   label_val = c("M", "F", "U", "UNDIFFERENTIATED"),
                   frmt_combine(
                     "{n} ({pct}%)",
                     n = frmt("XXX"),
                     pct = frmt("XX.X") )),

    frmt_structure(group_val = "Country",
                   label_val = c("CHN", "USA", "BRA", "PAK", "NGA", "RUS", "JPN", "GBR", "CAN", "NA"),
                   frmt_combine(
                     "{n} ({pct}%)",
                     n = frmt("XXX"),
                     pct = frmt("XX.X") ))
  ),

  # Align values on decimal places and spaces
  col_style_plan = col_style_plan(
    col_style_structure(col = matches("[A-Z]:.*"),
                        align = c(".", " "))
  ) ) %>%
  print_to_gt(demog_ard)
x.x: Study Subject Data
x.x.x: Demographic Characteristics. Table x.x.x.x: Demographic Characteristics - Full Analysis Set
A: Drug X B: Placebo C: Combination
Age (years)


  n 134          134          132         
  Mean (SD)  33.8 (6.6)   35.4 (7.9)   35.4 (7.7) 
  Median  33           35           35         
  Min - Max  21.0 - 50.0  21.0 - 62.0  20.0 - 69.0
Sex


  F  79 (59.0%)   82 (61.2%)   70 (53.0%) 
  M  55 (41.0%)   52 (38.8%)   62 (47.0%) 
Country


  CHN  74 (55.2%)   81 (60.4%)   64 (48.5%) 
  USA  10 ( 7.5%)   13 ( 9.7%)   17 (12.9%) 
  BRA  13 ( 9.7%)    7 ( 5.2%)   10 ( 7.6%) 
  PAK  12 ( 9.0%)    9 ( 6.7%)   10 ( 7.6%) 
  NGA   8 ( 6.0%)    7 ( 5.2%)   11 ( 8.3%) 
  RUS   5 ( 3.7%)    8 ( 6.0%)    6 ( 4.5%) 
  JPN   5 ( 3.7%)    4 ( 3.0%)    9 ( 6.8%) 
  GBR   4 ( 3.0%)    3 ( 2.2%)    2 ( 1.5%) 
  CAN   3 ( 2.2%)    2 ( 1.5%)    3 ( 2.3%) 

See this vignette for more details on formatting functions: link to website

See this vignette for the completed table example: link to website

4.2 Adverse Event Tables

We will use the ex_adae data included within the formatters package.

Code
head(formatters::ex_adae)
# A tibble: 6 × 48
  STUDYID USUBJID      SUBJID SITEID   AGE SEX   RACE  COUNTRY INVID ARM   ARMCD
  <chr>   <chr>        <chr>  <chr>  <int> <fct> <fct> <fct>   <chr> <fct> <fct>
1 AB12345 AB12345-BRA… id-134 BRA-1     47 M     WHITE BRA     BRA-1 A: D… ARM A
2 AB12345 AB12345-BRA… id-134 BRA-1     47 M     WHITE BRA     BRA-1 A: D… ARM A
3 AB12345 AB12345-BRA… id-134 BRA-1     47 M     WHITE BRA     BRA-1 A: D… ARM A
4 AB12345 AB12345-BRA… id-134 BRA-1     47 M     WHITE BRA     BRA-1 A: D… ARM A
5 AB12345 AB12345-BRA… id-141 BRA-1     35 F     WHITE BRA     BRA-1 C: C… ARM C
6 AB12345 AB12345-BRA… id-141 BRA-1     35 F     WHITE BRA     BRA-1 C: C… ARM C
# ℹ 37 more variables: ACTARM <fct>, ACTARMCD <fct>, STRATA1 <fct>,
#   STRATA2 <fct>, BMRKR1 <dbl>, BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>,
#   BMEASIFL <fct>, BEP01FL <fct>, RANDDT <date>, TRTSDTM <dttm>,
#   TRTEDTM <dttm>, EOSSTT <fct>, EOSDT <date>, EOSDY <int>, DCSREAS <fct>,
#   DTHDT <date>, LSTALVDT <date>, study_duration_secs <dbl>, ASEQ <int>,
#   AESEQ <int>, AETERM <fct>, AELLT <fct>, AEDECOD <fct>, AEHLT <fct>,
#   AEHLGT <fct>, AEBODSYS <fct>, AESOC <fct>, AESEV <fct>, AESER <fct>, …

4.2.1 rtables

Adverse Events by ID

Code
resetSession()
library(rtables)

s_events_patients <- function(x, labelstr, .N_col) {
  in_rows(
    "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")
  )
}

table_count_per_id <- function(df, .N_col, termvar = "AEDECOD", idvar = "USUBJID") {

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

  counts <- table(x[!duplicated(paste0(id, x))])

  in_rows(
    .list = lapply(counts,
                   function(xi) rcell(c(xi, xi/.N_col), "xx (xx.xx%)")),
    .labels = names(counts)
  )
}

lyt <- basic_table(show_colcounts = TRUE) %>%
    split_cols_by("ARM") %>%
    analyze("USUBJID", afun = s_events_patients) %>%
    split_rows_by("AEBODSYS", child_labels = "visible", 
                  split_fun = trim_levels_in_group("AEDECOD"),
                  section_div = " ") %>%
    summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
    analyze("AEDECOD", table_count_per_id, show_labels = "hidden", indent_mod = -1)

build_table(lyt, ex_adae, alt_counts_df = ex_adsl)
                                      A: Drug X      B: Placebo    C: Combination
                                       (N=134)        (N=134)         (N=132)    
—————————————————————————————————————————————————————————————————————————————————
Patients with at least one event     122 (91.04%)   123 (91.79%)    120 (90.91%) 
Total number of events                   609            622             703      
cl A.1                                                                           
  Patients with at least one event   78 (58.21%)    75 (55.97%)     89 (67.42%)  
  Total number of events                 132            130             160      
  dcd A.1.1.1.1                      50 (37.31%)    45 (33.58%)     63 (47.73%)  
  dcd A.1.1.1.2                      48 (35.82%)    48 (35.82%)     50 (37.88%)  
                                                                                 
cl B.1                                                                           
  Patients with at least one event   47 (35.07%)    49 (36.57%)     43 (32.58%)  
  Total number of events                  56             60              62      
  dcd B.1.1.1.1                      47 (35.07%)    49 (36.57%)     43 (32.58%)  
                                                                                 
cl B.2                                                                           
  Patients with at least one event   79 (58.96%)    74 (55.22%)     85 (64.39%)  
  Total number of events                 129            138             143      
  dcd B.2.1.2.1                      49 (36.57%)    44 (32.84%)     52 (39.39%)  
  dcd B.2.2.3.1                      48 (35.82%)    54 (40.30%)     51 (38.64%)  
                                                                                 
cl C.1                                                                           
  Patients with at least one event   43 (32.09%)    46 (34.33%)     43 (32.58%)  
  Total number of events                  55             63              64      
  dcd C.1.1.1.3                      43 (32.09%)    46 (34.33%)     43 (32.58%)  
                                                                                 
cl C.2                                                                           
  Patients with at least one event   35 (26.12%)    48 (35.82%)     55 (41.67%)  
  Total number of events                  48             53              65      
  dcd C.2.1.2.1                      35 (26.12%)    48 (35.82%)     55 (41.67%)  
                                                                                 
cl D.1                                                                           
  Patients with at least one event   79 (58.96%)    67 (50.00%)     80 (60.61%)  
  Total number of events                 127            106             135      
  dcd D.1.1.1.1                      50 (37.31%)    42 (31.34%)     51 (38.64%)  
  dcd D.1.1.4.2                      48 (35.82%)    42 (31.34%)     50 (37.88%)  
                                                                                 
cl D.2                                                                           
  Patients with at least one event   47 (35.07%)    58 (43.28%)     57 (43.18%)  
  Total number of events                  62             72              74      
  dcd D.2.1.5.3                      47 (35.07%)    58 (43.28%)     57 (43.18%)  

4.2.2 tern (+ rtables)

Code
resetSession()
library(tern)

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Patients with at least one event",
      nonunique = "Total number of events"
    )
  ) %>%
  split_rows_by(
      "AEBODSYS",
      child_labels = "visible",
      split_fun = drop_split_levels,
      section_div = " "
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Patients with at least one event",
      nonunique = "Total number of events"
    )
  ) %>%
  count_occurrences(vars = "AEDECOD", .indent_mods = -1L)

build_table(lyt, df = ex_adae, alt_counts_df = ex_adsl)
                                      A: Drug X    B: Placebo    C: Combination
                                       (N=134)       (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————
Patients with at least one event     122 (91.0%)   123 (91.8%)    120 (90.9%)  
Total number of events                   609           622            703      
cl A.1                                                                         
  Patients with at least one event   78 (58.2%)    75 (56.0%)      89 (67.4%)  
  Total number of events                 132           130            160      
  dcd A.1.1.1.1                      50 (37.3%)    45 (33.6%)      63 (47.7%)  
  dcd A.1.1.1.2                      48 (35.8%)    48 (35.8%)      50 (37.9%)  
                                                                               
cl B.1                                                                         
  Patients with at least one event   47 (35.1%)    49 (36.6%)      43 (32.6%)  
  Total number of events                 56            60              62      
  dcd B.1.1.1.1                      47 (35.1%)    49 (36.6%)      43 (32.6%)  
                                                                               
cl B.2                                                                         
  Patients with at least one event   79 (59.0%)    74 (55.2%)      85 (64.4%)  
  Total number of events                 129           138            143      
  dcd B.2.1.2.1                      49 (36.6%)    44 (32.8%)      52 (39.4%)  
  dcd B.2.2.3.1                      48 (35.8%)    54 (40.3%)      51 (38.6%)  
                                                                               
cl C.1                                                                         
  Patients with at least one event   43 (32.1%)    46 (34.3%)      43 (32.6%)  
  Total number of events                 55            63              64      
  dcd C.1.1.1.3                      43 (32.1%)    46 (34.3%)      43 (32.6%)  
                                                                               
cl C.2                                                                         
  Patients with at least one event   35 (26.1%)    48 (35.8%)      55 (41.7%)  
  Total number of events                 48            53              65      
  dcd C.2.1.2.1                      35 (26.1%)    48 (35.8%)      55 (41.7%)  
                                                                               
cl D.1                                                                         
  Patients with at least one event   79 (59.0%)    67 (50.0%)      80 (60.6%)  
  Total number of events                 127           106            135      
  dcd D.1.1.1.1                      50 (37.3%)    42 (31.3%)      51 (38.6%)  
  dcd D.1.1.4.2                      48 (35.8%)    42 (31.3%)      50 (37.9%)  
                                                                               
cl D.2                                                                         
  Patients with at least one event   47 (35.1%)    58 (43.3%)      57 (43.2%)  
  Total number of events                 62            72              74      
  dcd D.2.1.5.3                      47 (35.1%)    58 (43.3%)      57 (43.2%)  

4.2.3 gt

Code
resetSession()

library(tidyverse)
library(gt)

ex_adsl <- formatters::ex_adsl
ex_adae <- formatters::ex_adae

header_n <- ex_adsl |> 
  dplyr::group_by(ARM) |> 
  dplyr::summarize(
    N = dplyr::n_distinct(USUBJID)
  ) 

col_lbls <- header_n |> 
  dplyr::transmute(
    ARMN = sprintf("%s  \n  (N=%i)", ARM, N)
  ) |> 
  dplyr::group_split(ARMN) 

sum_ex <- merge(ex_adae, header_n, by = "ARM") |> 
  dplyr::group_by(ARM) |> 
  dplyr::summarize(
    n_oe = dplyr::n_distinct(USUBJID),
    pct_oe = n_oe/mean(N),
    n_tot = dplyr::n(),
    .groups = "drop"
  )

sum_aebodsys <- merge(ex_adae, header_n, by = "ARM") |> 
  dplyr::group_by(ARM, AEBODSYS) |> 
  dplyr::summarize(
    n_oe = dplyr::n_distinct(USUBJID),
    pct_oe = n_oe/mean(N),
    n_tot = dplyr::n(),
    .groups = "drop"
  )

sum_aedecod <- merge(ex_adae, header_n, by = "ARM") |> 
  dplyr::group_by(ARM, AEBODSYS, AEDECOD) |> 
  dplyr::summarize(
    n_oe = dplyr::n_distinct(USUBJID),
    pct_oe = n_oe/mean(N),
    .groups = "drop"
  )

ex_tbl <- dplyr::bind_rows(sum_ex, sum_aebodsys, sum_aedecod) |> 
  tidyr::pivot_longer(cols = c(n_oe, n_tot), names_to = "lbl", values_to = "n") |> 
  dplyr::mutate(
    pct_oe = ifelse(lbl == "n_tot", NA_real_, pct_oe)
  ) |> 
  pivot_wider(id_cols = c(AEBODSYS, AEDECOD, lbl), names_from = ARM, values_from = c(n, pct_oe)) |> 
  dplyr::mutate(
    AEDECOD = forcats::fct_relevel(
      .f = dplyr::case_when(
        is.na(AEDECOD) & lbl == "n_tot" ~ "Total number of events",
        is.na(AEDECOD) & lbl == "n_oe" ~ "Patients with at least one event",
        TRUE ~ AEDECOD
      ),
      c("Patients with at least one event", "Total number of events"),
      after = 0
      ),
    AEBODSYS = forcats::fct_relevel(
      forcats::fct_na_value_to_level(
        AEBODSYS,
        level = " "
        ),
      " ",
      after = 0
    )
  ) |> 
  dplyr::filter(!(lbl == "n_tot" & !(AEDECOD %in% c("Patients with at least one event", "Total number of events")))) |> 
  dplyr::arrange(AEBODSYS, AEDECOD)

ex_tbl |> 
  gt(
    rowname_col = "AEDECOD",
    groupname_col = "AEBODSYS"
  ) |> 
  cols_hide(columns = "lbl") |> 
  fmt_percent(
    columns = starts_with("pct"),
    decimals = 1
  ) |> 
  cols_merge_n_pct(
    col_n = "n_A: Drug X",
    col_pct = "pct_oe_A: Drug X"
  ) |> 
  cols_merge_n_pct(
    col_n = "n_B: Placebo",
    col_pct = "pct_oe_B: Placebo"
  ) |> 
  cols_merge_n_pct(
    col_n = "n_C: Combination",
    col_pct = "pct_oe_C: Combination"
  ) |> 
  cols_label(
    "n_A: Drug X" = md(col_lbls[[1]]),
    "n_B: Placebo" = md(col_lbls[[2]]),
    "n_C: Combination" = md(col_lbls[[3]])
  ) |> 
  cols_align(
    columns = 3:9,
    align = "center"
  ) |> 
  cols_align(
    columns = 1:2,
    align = "left"
  ) |> 
  cols_width(
    .list = list(
      1:2 ~ px(250),
      3:9 ~ px(120)
    )
  ) |> 
  tab_stub_indent(
    rows = 2:18,
    indent = 3
  )
A: Drug X
(N=134)
B: Placebo
(N=134)
C: Combination
(N=132)
Patients with at least one event 122 (91.0%) 123 (91.8%) 120 (90.9%)
Total number of events 609 622 703
cl A.1
Patients with at least one event 78 (58.2%) 75 (56.0%) 89 (67.4%)
Total number of events 132 130 160
dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%)
dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%)
cl B.1
Patients with at least one event 47 (35.1%) 49 (36.6%) 43 (32.6%)
Total number of events 56 60 62
dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%)
cl B.2
Patients with at least one event 79 (59.0%) 74 (55.2%) 85 (64.4%)
Total number of events 129 138 143
dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%)
dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%)
cl C.1
Patients with at least one event 43 (32.1%) 46 (34.3%) 43 (32.6%)
Total number of events 55 63 64
dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%)
cl C.2
Patients with at least one event 35 (26.1%) 48 (35.8%) 55 (41.7%)
Total number of events 48 53 65
dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%)
cl D.1
Patients with at least one event 79 (59.0%) 67 (50.0%) 80 (60.6%)
Total number of events 127 106 135
dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%)
dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%)
cl D.2
Patients with at least one event 47 (35.1%) 58 (43.3%) 57 (43.2%)
Total number of events 62 72 74
dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%)

4.2.4 tables

The tables package normally generates tables from single datasets, while this kind of table requires information from two: adsl and ex_adae. One way to handle this would be to add the adsl patient count information to a copy of the ex_adae table. In this code we use a different approach: we generate one table of patient counts to produce the heading lines, and a second table with the adverse event data, then use rbind() to combine the two tables.

Code
resetSession()

library(tables)
table_options(doCSS = TRUE)

ex_adae <- formatters::ex_adae

subject_counts <- table(adsl$ARM)

countpercentid <- function(num, ARM) {
  n <- length(unique(num))
  if (n == 0) pct <- 0
  else        pct <- 100*n/subject_counts[ARM[1]]
  sprintf("%d (%.2f%%)", 
          length(unique(num)), 
          pct)
}

count <- function(x) sprintf("(N=%d)", length(x))

heading <- tabular(Heading("")*1*
                     Heading("")*count ~ 
                   Heading()*ARM, data = adsl)

body <- tabular( Heading("Patients with at least one event")*1*
                   Heading("")*countpercentid*Arguments(ARM = ARM)*
                   Heading()*USUBJID +
                 Heading("Total number of events")*1*Heading("")*1 +
                 Heading()*AEBODSYS*
                   (Heading("Patients with at least one event")*
                      Percent(denom = ARM, fn = countpercentid)*
                      Heading()*USUBJID +
                    Heading("Total number of events")*1 +
                    Heading()*AEDECOD*DropEmpty(which = "row")*
                      Heading()*Percent(denom = ARM, fn = countpercentid)*
                      Heading()*USUBJID) ~ 
                 Heading()*ARM, 
                 data = ex_adae )

tab <- rbind(heading, body)
useGroupLabels(tab, indent = "&emsp;", extraLines = 1)
A: Drug X B: Placebo C: Combination
(N=134) (N=134) (N=132)
Patients with at least one event 122 (91.04%) 123 (91.79%) 120 (90.91%)
     
Total number of events 609 622 703
     
cl A.1      
 Patients with at least one event 78 (58.21%) 75 (55.97%) 89 (66.42%)
 Total number of events 132 130 160
 dcd A.1.1.1.1 50 (37.31%) 45 (33.58%) 63 (47.01%)
 dcd A.1.1.1.2 48 (35.82%) 48 (35.82%) 50 (37.31%)
     
cl B.1      
 Patients with at least one event 47 (35.07%) 49 (36.57%) 43 (32.09%)
 Total number of events 56 60 62
 dcd B.1.1.1.1 47 (35.07%) 49 (36.57%) 43 (32.09%)
     
cl B.2      
 Patients with at least one event 79 (58.96%) 74 (55.22%) 85 (63.43%)
 Total number of events 129 138 143
 dcd B.2.1.2.1 49 (36.57%) 44 (32.84%) 52 (38.81%)
 dcd B.2.2.3.1 48 (35.82%) 54 (40.30%) 51 (38.06%)
     
cl C.1      
 Patients with at least one event 43 (32.09%) 46 (34.33%) 43 (32.09%)
 Total number of events 55 63 64
 dcd C.1.1.1.3 43 (32.09%) 46 (34.33%) 43 (32.09%)
     
cl C.2      
 Patients with at least one event 35 (26.12%) 48 (35.82%) 55 (41.04%)
 Total number of events 48 53 65
 dcd C.2.1.2.1 35 (26.12%) 48 (35.82%) 55 (41.04%)
     
cl D.1      
 Patients with at least one event 79 (58.96%) 67 (50.00%) 80 (59.70%)
 Total number of events 127 106 135
 dcd D.1.1.1.1 50 (37.31%) 42 (31.34%) 51 (38.06%)
 dcd D.1.1.4.2 48 (35.82%) 42 (31.34%) 50 (37.31%)
     
cl D.2      
 Patients with at least one event 47 (35.07%) 58 (43.28%) 57 (42.54%)
 Total number of events 62 72 74
 dcd D.2.1.5.3 47 (35.07%) 58 (43.28%) 57 (42.54%)

4.2.5 flextable

By using tables::tabular() to create a table and then converting it to a flextable using as_flextable(), you can take advantage of the convenience and flexibility provided by the tables package while still benefiting from the formatting capabilities of flextable.

Code
library(flextable)
as_flextable(body, spread_first_col = TRUE, add_tab = TRUE) |>
  align(j = 1, part = "all", align = "left") |> 
  padding(padding = 4, part = "all") |> 
  add_header_row(
    values = c("", fmt_header_n(subject_counts, newline = FALSE)),
    top = FALSE) |> 
  hline(i = 1, part = "header", border = fp_border_default(width = 0))

A: Drug X

B: Placebo

C: Combination

(N=134)

(N=134)

(N=132)

Patients with at least one event

122 (91.04%)

123 (91.79%)

120 (90.91%)

Total number of events

609

622

703

cl A.1

Patients with at least one event

78 (58.21%)

75 (55.97%)

89 (66.42%)

Total number of events

132

130

160

dcd A.1.1.1.1

50 (37.31%)

45 (33.58%)

63 (47.01%)

dcd A.1.1.1.2

48 (35.82%)

48 (35.82%)

50 (37.31%)

cl B.1

Patients with at least one event

47 (35.07%)

49 (36.57%)

43 (32.09%)

Total number of events

56

60

62

dcd B.1.1.1.1

47 (35.07%)

49 (36.57%)

43 (32.09%)

cl B.2

Patients with at least one event

79 (58.96%)

74 (55.22%)

85 (63.43%)

Total number of events

129

138

143

dcd B.2.1.2.1

49 (36.57%)

44 (32.84%)

52 (38.81%)

dcd B.2.2.3.1

48 (35.82%)

54 (40.30%)

51 (38.06%)

cl C.1

Patients with at least one event

43 (32.09%)

46 (34.33%)

43 (32.09%)

Total number of events

55

63

64

dcd C.1.1.1.3

43 (32.09%)

46 (34.33%)

43 (32.09%)

cl C.2

Patients with at least one event

35 (26.12%)

48 (35.82%)

55 (41.04%)

Total number of events

48

53

65

dcd C.2.1.2.1

35 (26.12%)

48 (35.82%)

55 (41.04%)

cl D.1

Patients with at least one event

79 (58.96%)

67 (50.00%)

80 (59.70%)

Total number of events

127

106

135

dcd D.1.1.1.1

50 (37.31%)

42 (31.34%)

51 (38.06%)

dcd D.1.1.4.2

48 (35.82%)

42 (31.34%)

50 (37.31%)

cl D.2

Patients with at least one event

47 (35.07%)

58 (43.28%)

57 (42.54%)

Total number of events

62

72

74

dcd D.2.1.5.3

47 (35.07%)

58 (43.28%)

57 (42.54%)

4.2.6 tidytlg

Code
resetSession()
library(dplyr)
library(tidytlg)

adsl <- formatters::ex_adsl
adae <- formatters::ex_adae %>% 
  mutate(TRTEMFL = "Y")

# Create analysis population counts
tbl1 <- freq(adsl,
             rowvar = "SAFFL",
             colvar = "ARM",
             statlist = statlist("n"),
             rowtext = "Analysis Set: Safety Population",
             subset = SAFFL == "Y")

# Create counts (percentages) for patients with at least one event
tbl2 <- freq(adae,
             denom_df = adsl,
             rowvar = "TRTEMFL",
             colvar = "ARM",
             statlist = statlist("n (x.x%)"),
             rowtext = "Patients with at least one event",
             subset = TRTEMFL == "Y")

# Create counts (percentages) of AE by AEBODSYS and AEDECOD
tbl3a <- nested_freq(adae,
                    denom_df = adsl,
                    rowvar = "AEBODSYS*AEDECOD",
                    colvar = "ARM",
                    statlist = statlist("n (x.x%)"))

# Create total event counts by AEBODSYS
tbl3b <- freq(adae,
              rowvar = "AEBODSYS",
              colvar = "ARM",
              statlist = statlist("n", distinct = FALSE)) %>% 
  rename(AEBODSYS = label) %>% 
  mutate(label = "Total number of events",
         nested_level = 0)

# interleave tbl3a and tbl3b by AEBODSYS
tbl3 <- bind_rows(tbl3a, tbl3b) %>% 
  arrange(AEBODSYS, nested_level)

# combine analysis results together
tbl <- bind_table(tbl1, tbl2, tbl3) %>% 
  select(-AEBODSYS)

# output the analysis results
gentlg(huxme       = tbl,
       format      = "HTML",
       print.hux = FALSE,
       file        = "Table x.x.x.x",
       orientation = "portrait",
       title = "Adverse Events Summary - Safety Analysis Set",
       colheader = c("","A: Drug X","B: Placebo","C: Combination"))
[[1]]
  <div style='border-top :1pt solid; border-bottom :1pt solid; '>              
  <div style = "text-indent: -36px; padding-left: 36px;"> Table                
  x.x.x.x: &emsp; Adverse Events Summary - Safety Analysis                     
  Set</div>                                                                    
                                       <div           <div           <div      
                                   style='borde   style='borde   style='borde  
                                   r-bottom:1pt   r-bottom:1pt   r-bottom:1pt  
                                    solid'> A:     solid'> B:     solid'> C:   
                                      Drug X        Placebo      Combination   
  <div style='text-indent:             134            134            132       
  -17.76px; padding-left:                                                      
  17.76px'> Analysis Set: Safety                                               
  Population                                                                   
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:         122 (91.0%)    123 (91.8%)    120 (90.9%)   
  -17.76px; padding-left:                                                      
  17.76px'> Patients with at                                                   
  least one event                                                              
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:          78 (58.2%)     75 (56.0%)     89 (67.4%)   
  -17.76px; padding-left:                                                      
  17.76px'> cl A.1                                                             
  <div style='text-indent:             132            130            160       
  -17.76px; padding-left:                                                      
  35.52px'> Total number of                                                    
  events                                                                       
  <div style='text-indent:          50 (37.3%)     45 (33.6%)     63 (47.7%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd A.1.1.1.1                                                      
  <div style='text-indent:          48 (35.8%)     48 (35.8%)     50 (37.9%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd A.1.1.1.2                                                      
  <div style='text-indent:          47 (35.1%)     49 (36.6%)     43 (32.6%)   
  -17.76px; padding-left:                                                      
  17.76px'> cl B.1                                                             
  <div style='text-indent:              56             60             62       
  -17.76px; padding-left:                                                      
  35.52px'> Total number of                                                    
  events                                                                       
  <div style='text-indent:          47 (35.1%)     49 (36.6%)     43 (32.6%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd B.1.1.1.1                                                      
  <div style='text-indent:          79 (59.0%)     74 (55.2%)     85 (64.4%)   
  -17.76px; padding-left:                                                      
  17.76px'> cl B.2                                                             
  <div style='text-indent:             129            138            143       
  -17.76px; padding-left:                                                      
  35.52px'> Total number of                                                    
  events                                                                       
  <div style='text-indent:          49 (36.6%)     44 (32.8%)     52 (39.4%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd B.2.1.2.1                                                      
  <div style='text-indent:          48 (35.8%)     54 (40.3%)     51 (38.6%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd B.2.2.3.1                                                      
  <div style='text-indent:          43 (32.1%)     46 (34.3%)     43 (32.6%)   
  -17.76px; padding-left:                                                      
  17.76px'> cl C.1                                                             
  <div style='text-indent:              55             63             64       
  -17.76px; padding-left:                                                      
  35.52px'> Total number of                                                    
  events                                                                       
  <div style='text-indent:          43 (32.1%)     46 (34.3%)     43 (32.6%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd C.1.1.1.3                                                      
  <div style='text-indent:          35 (26.1%)     48 (35.8%)     55 (41.7%)   
  -17.76px; padding-left:                                                      
  17.76px'> cl C.2                                                             
  <div style='text-indent:              48             53             65       
  -17.76px; padding-left:                                                      
  35.52px'> Total number of                                                    
  events                                                                       
  <div style='text-indent:          35 (26.1%)     48 (35.8%)     55 (41.7%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd C.2.1.2.1                                                      
  <div style='text-indent:          79 (59.0%)     67 (50.0%)     80 (60.6%)   
  -17.76px; padding-left:                                                      
  17.76px'> cl D.1                                                             
  <div style='text-indent:             127            106            135       
  -17.76px; padding-left:                                                      
  35.52px'> Total number of                                                    
  events                                                                       
  <div style='text-indent:          50 (37.3%)     42 (31.3%)     51 (38.6%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd D.1.1.1.1                                                      
  <div style='text-indent:          48 (35.8%)     42 (31.3%)     50 (37.9%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd D.1.1.4.2                                                      
  <div style='text-indent:          47 (35.1%)     58 (43.3%)     57 (43.2%)   
  -17.76px; padding-left:                                                      
  17.76px'> cl D.2                                                             
  <div style='text-indent:              62             72             74       
  -17.76px; padding-left:                                                      
  35.52px'> Total number of                                                    
  events                                                                       
  <div style='text-indent:          47 (35.1%)     58 (43.3%)     57 (43.2%)   
  -17.76px; padding-left:                                                      
  35.52px'> dcd D.2.1.5.3                                                      
                        <div style='border-bottom:1pt solid'> [table           
  x.x.x.x.html][/home/runner/work/_temp/5a83a2a5-6d01-4ccf-8f57-0c47           
                                          2be60bd2] 28JUN2024, 18:06           

Column names: label, A: Drug X, B: Placebo, C: Combination

4.2.7 tfrmt

Rather than starting with an ADaM, tfrmt assumes users will start with an ARD (Analysis Results Dataset), because of this, making this table will be split into two parts, first to make the ARD and second to format the table.

Code
resetSession()
library(tidyverse)
library(tfrmt)

# Make ARD 
ex_adsl <- formatters::ex_adsl
ex_adae <- formatters::ex_adae 

big_n <- ex_adsl |> 
  dplyr::group_by(ARM) |> 
  dplyr::summarize(
    N = dplyr::n_distinct(USUBJID)
  ) 

adae_with_n <- ex_adae |> 
  dplyr::left_join(big_n, by = "ARM")

calc_tot_and_any <- function(.data){
  .data |>
    dplyr::reframe(
      n_subj = n_distinct(USUBJID),
      pct_subj = n_subj/N,
      n_evnts = n()
    ) |> 
    dplyr::distinct() |>
    tidyr::pivot_longer(c("n_subj", "pct_subj", "n_evnts")) |> 
    dplyr::mutate(label = dplyr::case_when(
      name %in% c("n_subj", "pct_subj") ~ "Patients with at least one event",
      name == "n_evnts" ~ "Total number of events"
    ))
}

overall <- adae_with_n |> 
  dplyr::group_by(ARM) |> 
  calc_tot_and_any() |>
  dplyr:: mutate(AEBODSYS = label)

bdysys_overall <- adae_with_n |> 
  dplyr::group_by(ARM, AEBODSYS) |> 
  calc_tot_and_any()

aeterm_sum <- adae_with_n |> 
  dplyr::group_by(ARM, AEBODSYS, AETERM) |> 
  dplyr::reframe(
      n_subj = n_distinct(USUBJID),
      pct_subj = n_subj/N) |> 
  dplyr::distinct() |>
  tidyr::pivot_longer(ends_with("subj")) |> 
  dplyr::rename(label = AETERM)

header_n <- big_n |> 
  dplyr::rename(value = N) |> 
  dplyr::mutate(name = "header_n")

ae_ard <- dplyr::bind_rows(
  overall, 
  bdysys_overall,
  aeterm_sum,
  header_n
)

## Format Table 
tfrmt(
  column = ARM,
  group = c("AEBODSYS"),
  param = name,
  value = value,
  label = label, 
) |>
  # Then we cam combine it with an n percent template 
tfrmt_n_pct(n = "n_subj",
            pct = "pct_subj",
  pct_frmt_when = frmt_when("==1" ~ "", 
                            ">.99" ~ "(>99%)", 
                            "==0" ~ "", 
                            "<.01" ~ "(<1%)", 
                            "TRUE" ~ frmt("(xx.x%)", transform = ~.*100))
  ) |>
  #Finally we are going to add some additional formatting
  tfrmt(
    body_plan = body_plan(
      frmt_structure("n_evnts" = frmt("XXX"))
    ),
    big_n = big_n_structure("header_n"),
    # Aligning on decimal places and spaces
    col_style_plan = col_style_plan(
      col_style_structure(col = matches("[A-Z]:.*"),
                          align = c(".", " "))
    )
  ) |> 
  print_to_gt(ae_ard)
A: Drug X N = 134 B: Placebo N = 134 C: Combination N = 132
Patients with at least one event 122 (91.0%) 123 (91.8%) 120 (90.9%)
Total number of events 609         622         703        
cl A.1


  Patients with at least one event  78 (58.2%)  75 (56.0%)  89 (67.4%)
  Total number of events 132         130         160        
  trm A.1.1.1.1  50 (37.3%)  45 (33.6%)  63 (47.7%)
  trm A.1.1.1.2  48 (35.8%)  48 (35.8%)  50 (37.9%)
cl B.1


  Patients with at least one event  47 (35.1%)  49 (36.6%)  43 (32.6%)
  Total number of events  56          60          62        
  trm B.1.1.1.1  47 (35.1%)  49 (36.6%)  43 (32.6%)
cl B.2


  Patients with at least one event  79 (59.0%)  74 (55.2%)  85 (64.4%)
  Total number of events 129         138         143        
  trm B.2.1.2.1  49 (36.6%)  44 (32.8%)  52 (39.4%)
  trm B.2.2.3.1  48 (35.8%)  54 (40.3%)  51 (38.6%)
cl C.1


  Patients with at least one event  43 (32.1%)  46 (34.3%)  43 (32.6%)
  Total number of events  55          63          64        
  trm C.1.1.1.3  43 (32.1%)  46 (34.3%)  43 (32.6%)
cl C.2


  Patients with at least one event  35 (26.1%)  48 (35.8%)  55 (41.7%)
  Total number of events  48          53          65        
  trm C.2.1.2.1  35 (26.1%)  48 (35.8%)  55 (41.7%)
cl D.1


  Patients with at least one event  79 (59.0%)  67 (50.0%)  80 (60.6%)
  Total number of events 127         106         135        
  trm D.1.1.1.1  50 (37.3%)  42 (31.3%)  51 (38.6%)
  trm D.1.1.4.2  48 (35.8%)  42 (31.3%)  50 (37.9%)
cl D.2


  Patients with at least one event  47 (35.1%)  58 (43.3%)  57 (43.2%)
  Total number of events  62          72          74        
  trm D.2.1.5.3  47 (35.1%)  58 (43.3%)  57 (43.2%)

4.3 Time to Event Analysis Tables

4.3.1 Data and models used throughout

Code
resetSession()
library(dplyr)
library(tidyr)
library(stringr)
library(purrr)
library(survival)

data("cadaette", package = "random.cdisc.data")
head(cadaette)
# A tibble: 6 × 66
  STUDYID USUBJID     SUBJID SITEID   AGE AGEU  SEX   RACE  ETHNIC COUNTRY DTHFL
  <chr>   <chr>       <chr>  <chr>  <int> <fct> <fct> <fct> <fct>  <fct>   <fct>
1 AB12345 AB12345-BR… id-105 BRA-1     38 YEARS M     BLAC… HISPA… BRA     N    
2 AB12345 AB12345-BR… id-105 BRA-1     38 YEARS M     BLAC… HISPA… BRA     N    
3 AB12345 AB12345-BR… id-105 BRA-1     38 YEARS M     BLAC… HISPA… BRA     N    
4 AB12345 AB12345-BR… id-105 BRA-1     38 YEARS M     BLAC… HISPA… BRA     N    
5 AB12345 AB12345-BR… id-105 BRA-1     38 YEARS M     BLAC… HISPA… BRA     N    
6 AB12345 AB12345-BR… id-105 BRA-1     38 YEARS M     BLAC… HISPA… BRA     N    
# ℹ 55 more variables: INVID <chr>, INVNAM <chr>, ARM <fct>, ARMCD <fct>,
#   ACTARM <fct>, ACTARMCD <fct>, TRT01P <fct>, TRT01A <fct>, TRT02P <fct>,
#   TRT02A <fct>, REGION1 <fct>, STRATA1 <fct>, STRATA2 <fct>, BMRKR1 <dbl>,
#   BMRKR2 <fct>, ITTFL <fct>, SAFFL <fct>, BMEASIFL <fct>, BEP01FL <fct>,
#   AEWITHFL <fct>, RANDDT <date>, TRTSDTM <dttm>, TRTEDTM <dttm>,
#   TRT01SDTM <dttm>, TRT01EDTM <dttm>, TRT02SDTM <dttm>, TRT02EDTM <dttm>,
#   AP01SDTM <dttm>, AP01EDTM <dttm>, AP02SDTM <dttm>, AP02EDTM <dttm>, …
Code
adtte <- cadaette %>% 
    dplyr::filter(PARAMCD == "AETTE2", SAFFL == "Y")

Cox Proportional Hazard fit:

Code
cph <- coxph(Surv(AVAL, CNSR==0) ~ TRT01A + STRATA1, ties = "exact", data = adtte)

Kaplan-Meier Model

Code
surv_tbl <- as.data.frame(summary(survfit(Surv(AVAL, CNSR==0) ~ TRT01A,
                                          data = adtte, conf.type = "log-log"))$table) %>%
    dplyr::mutate(TRT01A = factor(str_remove(row.names(.), "TRT01A="),
                                  levels = levels(adtte$TRT01A)),
                  ind = FALSE)
mn_footer_txt <- c("Serious adverse events are defined as (...). All p-values are exploratory.",
                   "Hazard ratios are from a stratified Cox model of serious adverse event hazard rate,",
                   "with terms for treatment groups and strata1. Ties were handled using the exact",
                   "method. Hazard ratios of Placebo Combination over Drug X are presented, an",
                   "HR < 1 denotes improvement compared to Drug X.")

stitle_txt <- c("x.x.x: Time to First Serious Adverse Event",
                "Table x.x.x.x: Safety Endpoint - Safety Analysis Set")
.kmState <- currentState()

4.3.2 rtables

Code
resetSession(.kmState)
library(rtables)

## this is exported in development version of rtables, and will be so in the
## next CRAN release
RefFootnote <- rtables:::RefFootnote

cnsr_counter <- function(df, .var, .N_col) {
    x <- df[!duplicated(df$USUBJID), .var]
    x <- x[x != "__none__"]
    lapply(table(x), function(xi) rcell(xi*c(1, 1/.N_col), format = "xx (xx.xx%)"))
}
            
a_count_subjs <- function(x, .N_col) {
    in_rows("Subjects with Adverse Events n (%)" = rcell(length(unique(x)) * c(1, 1 / .N_col),
                                                                           format = "xx (xx.xx%)"))
}

a_cph <- function(df, .var, .in_ref_col, .ref_full, full_cox_fit) {
    if(.in_ref_col) {
        ret <- replicate(3, list(rcell(NULL)))
    } else {
        curtrt <- df[[.var]][1]
        coefs <- coef(full_cox_fit)
        sel_pos <- grep(curtrt, names(coefs), fixed = TRUE)
        hrval <- exp(coefs[sel_pos])
        hrvalret <- rcell(hrval, format = "xx.x")
        sdf <- survdiff(Surv(AVAL, CNSR==0) ~ TRT01A + STRATA1,
                        data = rbind(df, .ref_full))
        pval <- (1-pchisq(sdf$chisq, length(sdf$n)-1))/2
        ci_val <- exp(unlist(confint(full_cox_fit)[sel_pos,]))
        ret <- list(rcell(hrval, format = "xx.x"),
                    rcell(ci_val, format = "(xx.x, xx.x)"),
                    rcell(pval, format = "x.xxxx | (<0.0001)"))
    }
    in_rows(.list = ret, .names = c("Hazard ratio",
                                    "95% confidence interval",
                                    "p-value (one-sided stratified log rank)"))
}

a_tte <- function(df, .var,  kp_table) {
    ind <- grep(df[[.var]][1], row.names(kp_table), fixed = TRUE)
    minmax <- range(df[["AVAL"]])

    mm_val_str <- format_value(minmax, format = "xx.x, xx.x")
    rowfn <- list()
    in_rows(Median = kp_table[ind, "median", drop = TRUE],
            "95% confidence interval" = unlist(kp_table[ind, c("0.95LCL", "0.95UCL")]),
            "Min Max" = mm_val_str,
            .formats = c("xx.xx",
                         "xx.xx - xx.xx",
                         "xx"),
            .cell_footnotes = list(NULL, NULL, list(RefFootnote("Denotes censoring", index = 0L, symbol = "*"))))
}
            

adtte2 <- adtte |>
    mutate(CNSDTDSC = ifelse(CNSDTDSC == "", "__none__", CNSDTDSC))

lyt <- basic_table(show_colcounts = TRUE,
                   title = "x.x: Safety Data",
                   subtitles = stitle_txt,
                   main_footer = mn_footer_txt,
                   prov_footer = "Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY") |>
    split_cols_by("ARM", ref_group = "A: Drug X") |>
    analyze("USUBJID", a_count_subjs, show_labels = "hidden") |>
    analyze("CNSDTDSC", cnsr_counter, var_labels = "Censored Subjects", show_labels = "visible") |>
    analyze("ARM", a_cph, extra_args = list(full_cox_fit = cph), show_labels = "hidden") |>
    analyze("ARM", a_tte, var_labels = "Time to first adverse event", show_labels = "visible",
            extra_args = list(kp_table = surv_tbl),
            table_names = "kapmeier")

tbl_tte <- build_table(lyt, adtte2)

fnotes_at_path(tbl_tte, c("ma_USUBJID_CNSDTDSC_ARM_kapmeier", "kapmeier")) <- "Product-limit (Kaplan-Meier) estimates."
tbl_tte
x.x: Safety Data
x.x.x: Time to First Serious Adverse Event
Table x.x.x.x: Safety Endpoint - Safety Analysis Set

————————————————————————————————————————————————————————————————————————————————————————
                                            A: Drug X      B: Placebo     C: Combination
                                             (N=134)         (N=134)         (N=132)    
————————————————————————————————————————————————————————————————————————————————————————
Subjects with Adverse Events n (%)        134 (100.00%)   134 (100.00%)   132 (100.00%) 
Censored Subjects                                                                       
  Clinical Cut Off                         10 (7.46%)       4 (2.99%)      14 (10.61%)  
  Completion or Discontinuation            13 (9.70%)       3 (2.24%)      16 (12.12%)  
  End of AE Reporting Period               22 (16.42%)      4 (2.99%)      14 (10.61%)  
Hazard ratio                                                   1.5             1.1      
95% confidence interval                                    (1.1, 1.9)       (0.8, 1.5)  
p-value (one-sided stratified log rank)                      0.0208           0.4619    
Time to first adverse event {1}                                                         
  Median                                      0.39            0.37             0.26     
  95% confidence interval                  0.23 - 0.60     0.25 - 0.46     0.18 - 0.34  
  Min Max                                 0.0, 3.0 {*}    0.0, 3.0 {*}     0.0, 3.0 {*} 
————————————————————————————————————————————————————————————————————————————————————————

{1} - Product-limit (Kaplan-Meier) estimates.
{*} - Denotes censoring
————————————————————————————————————————————————————————————————————————————————————————

Serious adverse events are defined as (...). All p-values are exploratory.
Hazard ratios are from a stratified Cox model of serious adverse event hazard rate,
with terms for treatment groups and strata1. Ties were handled using the exact
method. Hazard ratios of Placebo Combination over Drug X are presented, an
HR < 1 denotes improvement compared to Drug X.

Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY

4.3.3 tern (+rtables)

tern encapsulates the specific statistical choices used by Roche. In particulate, its implementation of the Cox pairwise analysis does not implement the one-tailed p-value strategy used in the rest of this chapter.

We will first showcase the pure tern solution, which has different p-values for this reason, and then implement a hybrid tern + explicit rtables solution which fully recreates the exact table generated by other systems.

Code
resetSession(.kmState)
library(tern)

## this is exported in development version of rtables, and will be so in the
## next CRAN release
RefFootnote <- rtables:::RefFootnote


adtte3 <- adtte
adtte3$is_event <- adtte$CNSR == 0
adtte3$CNSDTDSC[adtte$CNSDTDSC == ""] <- NA



lyt1 <- basic_table(show_colcounts = TRUE,
                   title = "x.x: Safety Data",
                   subtitles = stitle_txt,
                   main_footer = mn_footer_txt,
                   prov_footer = "Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY") |>
  split_cols_by("TRT01A", ref_group = "A: Drug X") |>
  count_values(
    "STUDYID",
    values = "AB12345",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Subjects with Adverse Events n (%)")
  ) |>
  count_occurrences(
    "CNSDTDSC",
    var_labels = "Censored Subjects",
    show_labels = "visible",
    .formats = c(count_fraction = "xx.xx (xx.xx%)")
  ) |>
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    control = control_coxph(pval_method = "log-rank", ties = "exact"),
    strat = "STRATA1",
    .stats = c("hr", "hr_ci", "pvalue"),
    .formats = c(hr = "xx.x", hr_ci = "(xx.x, xx.x)", pvalue = "xx.xxxx"),
    .labels = c(hr = "Hazard ratio", hr_ci = "95% confidence interval", pvalue = "p-value (stratified log rank)"),
    show_labels = "hidden",
    table_names = "coxph"
  ) |>
  surv_time(
    vars = "AVAL",
    is_event = "is_event",
    control = control_surv_time(conf_type = "log-log"),
    .stats = c("median", "median_ci", "range"),
    .formats = c(median = "xx.xx", median_ci = "xx.xx - xx.xx", range = "xx.x, xx.x"),
    .labels = c(median_ci = "95% confidence interval", range = "Min Max"),
    .indent_mods = c(median_ci = 0L),
    var_labels = "Time to first adverse event"
  )

tbl_tte_tern <- build_table(lyt = lyt1, df = adtte3)
Warning: The `strat` argument of `s_coxph_pairwise()` is deprecated as of tern 0.9.3.
ℹ Please use the `strata` argument instead.
ℹ The deprecated feature was likely used in the rtables package.
  Please report the issue at
  <https://github.com/insightsengineering/rtables/issues>.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
generated.
Code
fnotes_at_path(tbl_tte_tern, c("ma_STUDYID_CNSDTDSC_coxph_AVAL", "AVAL")) <- "Product-limit (Kaplan-Meier) estimates."

fnote <- RefFootnote("Censored.", index = 0L, symbol = "^")

for(pth in col_paths(tbl_tte_tern)) {
    fnotes_at_path(tbl_tte_tern,
                   rowpath = c("ma_STUDYID_CNSDTDSC_coxph_AVAL", "AVAL", "Min Max"),
                   colpath = pth) <- fnote
}
tbl_tte_tern
x.x: Safety Data
x.x.x: Time to First Serious Adverse Event
Table x.x.x.x: Safety Endpoint - Safety Analysis Set

———————————————————————————————————————————————————————————————————————————————————
                                       A: Drug X      B: Placebo     C: Combination
                                        (N=134)         (N=134)         (N=132)    
———————————————————————————————————————————————————————————————————————————————————
Subjects with Adverse Events n (%)   134 (100.00%)   134 (100.00%)   132 (100.00%) 
Censored Subjects                                                                  
  Clinical Cut Off                     10 (7.5%)       4 (3.0%)        14 (10.6%)  
  Completion or Discontinuation        13 (9.7%)       3 (2.2%)        16 (12.1%)  
  End of AE Reporting Period          22 (16.4%)       4 (3.0%)        14 (10.6%)  
Hazard ratio                                              1.5             1.1      
95% confidence interval                               (1.2, 2.0)       (0.8, 1.5)  
p-value (stratified log rank)                           0.0023           0.6027    
Time to first adverse event {1}                                                    
  Median                                 0.39            0.37             0.26     
  95% confidence interval             0.23 - 0.60     0.25 - 0.46     0.18 - 0.34  
  Min Max                            0.0, 3.0 {^}    0.0, 3.0 {^}     0.0, 3.0 {^} 
———————————————————————————————————————————————————————————————————————————————————

{1} - Product-limit (Kaplan-Meier) estimates.
{^} - Censored.
———————————————————————————————————————————————————————————————————————————————————

Serious adverse events are defined as (...). All p-values are exploratory.
Hazard ratios are from a stratified Cox model of serious adverse event hazard rate,
with terms for treatment groups and strata1. Ties were handled using the exact
method. Hazard ratios of Placebo Combination over Drug X are presented, an
HR < 1 denotes improvement compared to Drug X.

Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY

We now create the hybrid table where we utilize a custom analysis function to recreate the one-sided p-values while using tern for the rest of the table structure.

Code
## this is exported in development version of rtables, and will be so in the
## next CRAN release
RefFootnote <- rtables:::RefFootnote

a_cph <- function(df, .var, .in_ref_col, .ref_full, full_cox_fit) {
  if(.in_ref_col) {
    ret <- replicate(3, list(rcell(NULL)))
  } else {
    curtrt <- df[[.var]][1]
    coefs <- coef(full_cox_fit)
    sel_pos <- grep(curtrt, names(coefs), fixed = TRUE)
    hrval <- exp(coefs[sel_pos])
    hrvalret <- rcell(hrval, format = "xx.x")
    sdf <- survival::survdiff(Surv(AVAL, CNSR==0) ~ TRT01A + STRATA1,
                              data = rbind(df, .ref_full))
    pval <- (1-pchisq(sdf$chisq, length(sdf$n)-1))/2
    ci_val <- exp(unlist(confint(full_cox_fit)[sel_pos,]))
    ret <- list(rcell(hrval, format = "xx.x"),
                rcell(ci_val, format = "(xx.x, xx.x)"),
                rcell(pval, format = "x.xxxx | (<0.0001)"))
  }
  in_rows(.list = ret, .names = c("Hazard ratio",
                                  "95% confidence interval",
                                  "p-value (one-sided stratified log rank)"))
}

lyt2 <- basic_table(show_colcounts = TRUE,
                   title = "x.x: Safety Data",
                   subtitles = stitle_txt,
                   main_footer = mn_footer_txt,
                   prov_footer = "Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY") |>
  split_cols_by("TRT01A", ref_group = "A: Drug X") |>
  count_values(
    "STUDYID",
    values = "AB12345",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Subjects with Adverse Events n (%)")
  ) |>
  count_occurrences(
    "CNSDTDSC",
    var_labels = "Censored Subjects",
    show_labels = "visible",
    .formats = c(count_fraction = "xx.xx (xx.xx%)")
  ) |>
  analyze("ARM", a_cph, extra_args = list(full_cox_fit = cph), show_labels = "hidden") |>
  surv_time(
    vars = "AVAL",
    is_event = "is_event",
    control = control_surv_time(conf_type = "log-log"),
    .stats = c("median", "median_ci", "range"),
    .formats = c(median = "xx.xx", median_ci = "xx.xx - xx.xx", range = "xx.x, xx.x"),
    .labels = c(median_ci = "95% confidence interval", range = "Min Max"),
    .indent_mods = c(median_ci = 0L),
    var_labels = "Time to first adverse event"
  )

tbl_tte_tern2 <- build_table(lyt = lyt2, df = adtte3)

fnotes_at_path(tbl_tte_tern2, c("ma_STUDYID_CNSDTDSC_ARM_AVAL", "AVAL")) <- "Product-limit (Kaplan-Meier) estimates."

fnote <- RefFootnote("Denotes censoring.", index = 0L, symbol = "*")

for(pth in col_paths(tbl_tte_tern2)) {
    fnotes_at_path(tbl_tte_tern2,
                   rowpath = c("ma_STUDYID_CNSDTDSC_ARM_AVAL", "AVAL", "Min Max"),
                   colpath = pth) <- fnote
}
tbl_tte_tern2
x.x: Safety Data
x.x.x: Time to First Serious Adverse Event
Table x.x.x.x: Safety Endpoint - Safety Analysis Set

————————————————————————————————————————————————————————————————————————————————————————
                                            A: Drug X      B: Placebo     C: Combination
                                             (N=134)         (N=134)         (N=132)    
————————————————————————————————————————————————————————————————————————————————————————
Subjects with Adverse Events n (%)        134 (100.00%)   134 (100.00%)   132 (100.00%) 
Censored Subjects                                                                       
  Clinical Cut Off                          10 (7.5%)       4 (3.0%)        14 (10.6%)  
  Completion or Discontinuation             13 (9.7%)       3 (2.2%)        16 (12.1%)  
  End of AE Reporting Period               22 (16.4%)       4 (3.0%)        14 (10.6%)  
Hazard ratio                                                   1.5             1.1      
95% confidence interval                                    (1.1, 1.9)       (0.8, 1.5)  
p-value (one-sided stratified log rank)                      0.0208           0.4619    
Time to first adverse event {1}                                                         
  Median                                      0.39            0.37             0.26     
  95% confidence interval                  0.23 - 0.60     0.25 - 0.46     0.18 - 0.34  
  Min Max                                 0.0, 3.0 {*}    0.0, 3.0 {*}     0.0, 3.0 {*} 
————————————————————————————————————————————————————————————————————————————————————————

{1} - Product-limit (Kaplan-Meier) estimates.
{*} - Denotes censoring.
————————————————————————————————————————————————————————————————————————————————————————

Serious adverse events are defined as (...). All p-values are exploratory.
Hazard ratios are from a stratified Cox model of serious adverse event hazard rate,
with terms for treatment groups and strata1. Ties were handled using the exact
method. Hazard ratios of Placebo Combination over Drug X are presented, an
HR < 1 denotes improvement compared to Drug X.

Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY

4.3.4 Cell Value Derivation for gt of Time to Event Analysis

Our standard TTE table consists of (a derivation of) four main parts:

  1. Descriptive stats including the number of subjects with an event, number of subjects censored and censoring reasons
  2. Hazard ratio with corresponding 95% CI from a (stratified) Cox model and a p-value from a stratified log rank test
  3. Median time to event Kaplan-Meier analysis
  4. Number of patients at risk at specified visits from Kaplan-Meier analysis (omitted here).
Code
resetSession(.kmState)
library(gt)
### Subject Count with events

## surv_tbl calculated above


subj_count <- surv_tbl  |> 
  dplyr::mutate(pct = sprintf("%i (%5.1f)", events, 100*events/records),
                label = "Number of subjects with serious adverse event, n (%)") |>
  dplyr::select(label, TRT01A, pct) |>
  tidyr::pivot_wider(id_cols = label, names_from = TRT01A, values_from = pct) |>
  dplyr::mutate(ind = FALSE)

# Number of censored subjects

cnsrd_subj_full <- surv_tbl |>
  dplyr::mutate(pct = sprintf("%i (%4.1f)", records-events, 100*(records-events)/records),
                CNSDTDSC = "Number of censored subjects, n (%)") |> 
  dplyr::select(CNSDTDSC, TRT01A, pct)
  
cnsrd_subj <- adtte |> 
  dplyr::group_by(TRT01A) |>
  dplyr::mutate(CNSR = CNSR/n()) |>
  dplyr::ungroup() |>
  dplyr::filter(CNSR != 0) |>
  dplyr::group_by(TRT01A, CNSDTDSC) |>
  dplyr::summarise(pct = sprintf("%i (%4.1f)", sum(CNSR != 0), 100*sum(CNSR)), .groups = "drop") |>
  dplyr::bind_rows(cnsrd_subj_full) |> 
  tidyr::pivot_wider(id_cols = CNSDTDSC, names_from = TRT01A, values_from = pct) |>
  dplyr::rename(label = CNSDTDSC) |>
  dplyr::mutate(ind = label != "Number of censored subjects, n (%)") |>
  dplyr::arrange(ind)
Code
## cph calculated above
hr <- exp(coef(cph))
ci_hr <- exp(confint(cph))

# Hazard ratio and 95% CI

df_hr <- cbind(ci_hr, hr) |>
  as.data.frame() |>
  (\(data) dplyr::filter(data, grepl("TRT01A", row.names(data))))() |> 
  (\(data) dplyr::mutate(
    data, 
    TRT01A = factor(stringr::str_remove(row.names(data), "TRT01A")),
    ci = sprintf("[%4.1f, %4.1f]", round(!!sym("2.5 %"), 1), round(!!sym("97.5 %"), 1))
    ))() |> 
  dplyr::select(TRT01A, hr, ci)

# Log rank p-value

log_rank_test <- purrr::map_df(.x = list(c("A: Drug X", "B: Placebo"),
                                         c("A: Drug X", "C: Combination")),
                               .f = ~{sdf <- survdiff(Surv(AVAL, CNSR==0) ~ TRT01A + STRATA1,
                                                      data = adtte |> dplyr::filter(TRT01A %in% .x));
                               data.frame(TRT01A = .x[2],
                                          pval = (1-pchisq(sdf$chisq, length(sdf$n)-1))/2)})

df_hr_comp <- merge(df_hr, log_rank_test, by = "TRT01A") |>
  dplyr::mutate(hr = sprintf("%4.1f", round(hr, 1)),
                pval = ifelse(pval < 0.0001, "<0.0001", sprintf("%6.4f", round(pval, 4)))) |>
  tidyr::pivot_longer(cols = c(hr, ci, pval), names_to = "label", values_to = "val") |>
  tidyr::pivot_wider(names_from = TRT01A, values_from = "val") |>
  dplyr::mutate(label = dplyr::recode(label,
                                      "hr" = "Hazard ratio",
                                      "ci" = "95% confidence interval",
                                      "pval" = "p-value (one-sided stratified log rank)"),
                ind = FALSE)
Code
median_survtime <- surv_tbl |>
  dplyr::mutate(ci = sprintf("[%4.2f, %4.2f]", !!sym("0.95LCL"), !!sym("0.95UCL")),
                median = sprintf("%4.2f", median),
                id = "") |>
  dplyr::select(TRT01A, id, median, ci) |>
  tidyr::pivot_longer(cols = c(id, median, ci), names_to = "label", values_to = "val") |>
  tidyr::pivot_wider(names_from = TRT01A, values_from = val) |>
  dplyr::mutate(ind = label != "id",
                label = dplyr::recode(label, "median" = "Median (years)",
                                      "ci" = "95% confidence interval",
                                      "id" = "Time to first serious adverse event (a)"))

min_max <- adtte |>
  dplyr::filter(!(AVAL == 0 & CNSR == 1)) |> 
  dplyr::group_by(TRT01A) |>
  dplyr::mutate(max_cnsr = !is.na(AVAL) & AVAL == max(AVAL, na.rm = TRUE) & CNSR == 1) |>
  dplyr::summarize(
    min_max = sprintf("%4.2f, %4.2f%s", min(AVAL, na.rm = TRUE), max(AVAL, na.rm = TRUE), ifelse(sum(max_cnsr) > 0, "*", "")),
    .groups = "drop"
    ) |>
  tidyr::pivot_wider(names_from = TRT01A, values_from = min_max) |>
  dplyr::mutate(label = "Min, Max (b)",
                ind = TRUE)

model_sum <- dplyr::bind_rows(subj_count, cnsrd_subj, df_hr_comp, median_survtime, min_max)

4.3.5 gt

Code
header_n <- adtte |> 
  dplyr::group_by(TRT01A) |>
  dplyr::summarise(N = dplyr::n(), .groups = "drop") |> 
  dplyr::transmute(TRT = sprintf("%s  \n  N=%i (100%%)", TRT01A, N)) |> 
  dplyr::group_split(TRT)


### Begin table creation

gt(model_sum) |> 
  cols_hide(ind) |>
  tab_header(
    title = "x.x: Safety Data",
    subtitle = md("x.x.x: Time to First Serious Adverse Event  \n  Table x.x.x.x: Safety Endpoint - Safety Analysis Set"),
    preheader = c("Protocol: XXXXX", "Cutoff date: DDMMYYYY")
  ) |> 
  tab_source_note("Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY") |>
  opt_align_table_header(align = "left") |>
  cols_align(align = c("center"),
             columns = c("A: Drug X", "B: Placebo", "C: Combination")) |>
  cols_align(align = "left",
             columns = "label") |>
  tab_style(style = cell_text(indent = pct(5)),
            locations = cells_body(columns = 1,
                                   rows = ind == TRUE)) |>
  sub_missing(columns = everything(), missing_text = "") |>
  cols_label("label" = "",
             "A: Drug X" = md(header_n[[1]]),
             "B: Placebo" = md(header_n[[2]]),
             "C: Combination" = md(header_n[[3]])) |> 
  tab_footnote(footnote = md("Serious adverse events are defines as (...). All p-values are exploratory.  \n  (a) Product-limit (Kaplan-Meier) estimates.   \n  (b) Minimum and maximum of event times. * Denotes censoring.  \n  Hazard ratios are from a stratified Cox model of serious adverse event hazard rate, with terms for treatment groups and strata1. Ties were handled using the exact method. Hazard ratios of Placebo/ Combination over Drug X are presented, a HR < 1 denotes improvement compared to Drug X.")) |> 
  tab_options(
    table.font.names = "Courier new",
    table.font.size = 9,
    page.orientation = "landscape",
    page.numbering = TRUE,
    page.header.use_tbl_headings = TRUE,
    page.footer.use_tbl_notes = TRUE)
x.x: Safety Data
x.x.x: Time to First Serious Adverse Event
Table x.x.x.x: Safety Endpoint - Safety Analysis Set
A: Drug X
N=134 (100%)
B: Placebo
N=134 (100%)
C: Combination
N=132 (100%)
Number of subjects with serious adverse event, n (%) 89 ( 66.4) 123 ( 91.8) 88 ( 66.7)
Number of censored subjects, n (%) 45 (33.6) 11 ( 8.2) 44 (33.3)
Clinical Cut Off 10 ( 7.5) 4 ( 3.0) 14 (10.6)
Completion or Discontinuation 13 ( 9.7) 3 ( 2.2) 16 (12.1)
End of AE Reporting Period 22 (16.4) 4 ( 3.0) 14 (10.6)
Hazard ratio
1.5 1.1
95% confidence interval
[ 1.1, 1.9] [ 0.8, 1.5]
p-value (one-sided stratified log rank)
0.0208 0.4619
Time to first serious adverse event (a)
Median (years) 0.39 0.37 0.26
95% confidence interval [0.23, 0.60] [0.25, 0.46] [0.18, 0.34]
Min, Max (b) 0.00, 3.00* 0.01, 3.00* 0.00, 3.00*
Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY
Serious adverse events are defines as (…). All p-values are exploratory.
(a) Product-limit (Kaplan-Meier) estimates.
(b) Minimum and maximum of event times. * Denotes censoring.
Hazard ratios are from a stratified Cox model of serious adverse event hazard rate, with terms for treatment groups and strata1. Ties were handled using the exact method. Hazard ratios of Placebo/ Combination over Drug X are presented, a HR < 1 denotes improvement compared to Drug X.

4.3.6 tables

Code
resetSession(.kmState)

library(tables)
table_options(doCSS = TRUE)

ex_adae <- formatters::ex_adae

subject_counts <- table(adsl$ARM)

countpercentid <- function(num, ARM) {
  n <- length(unique(num))
  if (n == 0) pct <- 0
  else        pct <- 100*n/subject_counts[ARM[1]]
  sprintf("%d (%.2f%%)", 
          length(unique(num)), 
          pct)
}

valuepercent <- function(x, ARM) {
  sprintf("%d (%.2f%%)", x, 100*x/subject_counts[ARM] )
}

blanks <- function(x) ""

count <- function(x) sprintf("(N=%d)", length(x))

hazardratio <- function(ARM) {
  entry <- paste0("TRT01A", ARM)
  coef <- coef(cph)
  if (entry %in% names(coef)) sprintf("%.1f", exp(coef[entry]))
  else ""
}

hazardratioconfint <- function(ARM) {
  entry <- paste0("TRT01A", ARM)
  confint <- confint(cph)
  if (entry %in% rownames(confint)) {
    confint <- as.numeric(confint[entry,])
    sprintf("(%.1f, %.1f)", exp(confint[1]), exp(confint[2]))
  } else ""
}

hazardpvalue <- function(ARM) {
  if (ARM == "A: Drug X") ""
  else {
    twogroups <- c("A: Drug X", ARM)
    sdf <- survdiff(Surv(AVAL, CNSR==0) ~ TRT01A + STRATA1,
                   data = adtte, subset = TRT01A %in% twogroups)
    pval <- (1-pchisq(sdf$chisq, length(sdf$n)-1))/2
    sprintf("%.4f", pval)
  }
}

Median <- function(ARM) {
  vals <- subset(surv_tbl, TRT01A == ARM)
  sprintf("%.2f", vals$median)
}

minmaxevent <- function(ARM) {
  vals <- subset(adtte, TRT01A == ARM)
  sprintf("%.2f, %.2f", min(vals$AVAL), max(vals$AVAL))
}

eventCI <- function(ARM) {
  vals <- subset(surv_tbl, TRT01A == ARM)
  sprintf("[%.2f, %.2f]", vals$`0.95LCL`, vals$`0.95UCL`)
}

heading <- tabular(Heading("")*1*Heading("")*count ~ 
                   Heading()*ARM, 
                   data = adsl)

part1 <- tabular( Heading("Subjects with serious adverse events")*1*Heading("")*
                    events*Heading()*
                    valuepercent*Arguments(ARM = TRT01A) ~ 
                  Heading()*TRT01A, 
                  data = surv_tbl )

part2 <- tabular( Heading("Number of censored subjects")*1*Factor(CNSDTDSC, "")*
                Heading()*countpercentid*Arguments(ARM = TRT01A)*
                    Heading()*USUBJID ~
                  Heading()*TRT01A, 
                  data = subset(adtte, nchar(CNSDTDSC) > 0))

part3 <- tabular( ( Heading("Hazard ratio")*1*Heading("")*hazardratio +
                    Heading("95% confidence interval")*1*Heading("")*hazardratioconfint +
                    Heading("p-value (one-sided stratified log rank)")*1*Heading("")*hazardpvalue +
                    Heading("Time to first serious adverse event")*1*(
                      Heading("Median (years)")*Median +
                      Heading("95% confidence interval")*eventCI +
                      Heading("Min, Max")*minmaxevent))*
                    Heading()*as.character(TRT01A)  ~
                  Heading()*TRT01A,
                  data = surv_tbl)

useGroupLabels(rbind(heading, part1, part2, part3),
               indent = "&emsp;")
A: Drug X B: Placebo C: Combination
(N=134) (N=134) (N=132)
Subjects with serious adverse events 89 (66.42%) 123 (91.79%) 88 (66.67%)
Number of censored subjects      
 Clinical Cut Off 10 (7.46%) 4 (2.99%) 14 (10.61%)
 Completion or Discontinuation 13 (9.70%) 3 (2.24%) 16 (12.12%)
 End of AE Reporting Period 22 (16.42%) 4 (2.99%) 14 (10.61%)
Hazard ratio   1.5 1.1
95% confidence interval   (1.1, 1.9) (0.8, 1.5)
p-value (one-sided stratified log rank)   0.0208 0.4619
Time to first serious adverse event      
 Median (years) 0.39 0.37 0.26
 95% confidence interval [0.23, 0.60] [0.25, 0.46] [0.18, 0.34]
 Min, Max 0.00, 3.00 0.00, 3.00 0.00, 3.00

4.3.7 flextable

This is a situation where the code required to create a flextable directly becomes too long or complex. In such case, it is more convenient to leverage existing functions from other packages to generate a tabular object and then convert it to a flextable using the as_flextable() method. Here we reuse the tables objects created in the previous section.

Code
library(flextable)
rbind(part1, part2, part3) |> 
  as_flextable(spread_first_col = TRUE, add_tab = TRUE) |>
  align(j = 1, part = "all", align = "left") |> 
  padding(padding = 4, part = "all") |> 
  add_header_row(
    values = c("", fmt_header_n(subject_counts, newline = FALSE)),
    top = FALSE
  ) |> 
  hline(i = 1, part = "header", border = fp_border_default(width = 0))

A: Drug X

B: Placebo

C: Combination

(N=134)

(N=134)

(N=132)

Subjects with serious adverse events

89 (66.42%)

123 (91.79%)

88 (66.67%)

Number of censored subjects

Clinical Cut Off

10 (7.46%)

4 (2.99%)

14 (10.61%)

Completion or Discontinuation

13 (9.70%)

3 (2.24%)

16 (12.12%)

End of AE Reporting Period

22 (16.42%)

4 (2.99%)

14 (10.61%)

Hazard ratio

1.5

1.1

95% confidence interval

(1.1, 1.9)

(0.8, 1.5)

p-value (one-sided stratified log rank)

0.0208

0.4619

Time to first serious adverse event

Median (years)

0.39

0.37

0.26

95% confidence interval

[0.23, 0.60]

[0.25, 0.46]

[0.18, 0.34]

Min, Max

0.00, 3.00

0.00, 3.00

0.00, 3.00

4.3.8 tidytlg

Code
resetSession(.kmState)
library(dplyr)
library(tidytlg)
library(broom)
library(stringr)

# Create analysis population counts
tbl1 <- freq(adtte,
             rowvar = "SAFFL",
             colvar = "TRT01A",
             statlist = statlist("n"),
             rowtext = "Analysis Set: Safety Population",
             subset = SAFFL == "Y")

# Create counts (percentages) for subjects with SAE
tbl2 <- freq(adtte,
             rowvar = "CNSR",
             colvar = "TRT01A",
             statlist = statlist("n (x.x%)"),
             rowtext = "Number of subjects with serious adverse events, n(%)",
             subset = CNSR == 0)

# Create counts (percentages) for subjects with SAE
tbl3a <- freq(adtte,
             rowvar = "CNSR",
             colvar = "TRT01A",
             statlist = statlist("n (x.x%)"),
             rowtext = "Number of censored subjects, n(%)",
             subset = CNSR == 1)

tbl3b <- freq(adtte,
             rowvar = "CNSDTDSC",
             colvar = "TRT01A",
             statlist = statlist("n (x.x%)"),
             subset = CNSR == 1)

tbl3 <- bind_rows(tbl3a, tbl3b)

# CoxPH model
coxmod <- tidy(cph, exponentiate = TRUE, conf.int = TRUE, conf.level = 0.95) %>% 
  filter(str_detect(term, "TRT01A")) %>% 
  mutate(term = str_remove(term, "TRT01A"))

tbl4a <- coxmod %>% 
  mutate(hr = roundSAS(estimate, digits = 2, as_char = TRUE)) %>% 
  select(term, hr) %>% 
  pivot_wider(names_from = "term", values_from = "hr") %>% 
  mutate(label = "Hazard ratio",
         row_type = "HEADER")

tbl4b <- coxmod %>% 
  mutate(across(c(conf.low, conf.high), ~roundSAS(.x, digits = 2)),
         ci = paste0("(", conf.low, ", ", conf.high, ")")) %>% 
  select(term, ci) %>% 
  pivot_wider(names_from = "term", values_from = "ci") %>% 
  mutate(label = "95% Confidence Interval",
         row_type = "VALUE")

tbl4 <- bind_rows(tbl4a, tbl4b) %>% 
  mutate(group_level = 0)

# Logrank test
log_rank_test <- purrr::map_df(.x = list(c("A: Drug X", "B: Placebo"),
                                         c("A: Drug X", "C: Combination")),
                               .f = ~{sdf <- survdiff(Surv(AVAL, CNSR==0) ~ TRT01A + STRATA1,
                                                      data = adtte %>% dplyr::filter(TRT01A %in% .x));
                               data.frame(TRT01A = .x[2],
                                          pval = (1-pchisq(sdf$chisq, length(sdf$n)-1))/2)})

tbl5 <- log_rank_test %>% 
  mutate(pval = roundSAS(pval, digits = 3, as_char = TRUE)) %>% 
  pivot_wider(names_from = "TRT01A", values_from = "pval") %>% 
  mutate(label = "p-value (one-sided stratified log rank)",
         row_type = "HEADER",
         group_level = 0)

# surv time stats
tbl6a <- surv_tbl %>% 
  mutate(median = roundSAS(median, digits = 2, as_char = TRUE)) %>% 
  select(TRT01A, median) %>% 
  pivot_wider(names_from = "TRT01A", values_from = "median") %>% 
  mutate(label = "Median (years)",
         row_type = "VALUE") %>% 
  add_row(label = "Time to first serious adverse event (1)", row_type = "HEADER", .before = 1)

tbl6b <- surv_tbl %>% 
  mutate(across(c(`0.95LCL`, `0.95UCL`), ~roundSAS(.x, digits = 2, as_char = TRUE)),
         ci = paste0("(", `0.95LCL`, ", ", `0.95UCL`, ")")) %>% 
  select(TRT01A, ci) %>% 
  pivot_wider(names_from = "TRT01A", values_from = "ci") %>% 
  mutate(label = "95% Confidence Interval",
         row_type = "VALUE")

tbl6c <- adtte %>% 
  filter(!(AVAL == 0 & CNSR == 1)) %>% 
  group_by(TRT01A) %>% 
  mutate(max_cnsr = !is.na(AVAL) & AVAL == max(AVAL, na.rm = TRUE) & CNSR == 1) %>% 
  summarise(
        min = min(AVAL, na.rm = TRUE),
        max = max(AVAL, na.rm = TRUE),
        is_censored = sum(max_cnsr) > 0) %>% 
  mutate(across(c(min, max), ~roundSAS(.x, digits = 2, as_char = TRUE)),
         min_max = ifelse(is_censored, paste0("(", min, ", ", max, "*)"), 
                          paste0("(", min, ", ", max, ")"))) %>% 
  select(TRT01A, min_max) %>% 
  pivot_wider(names_from = "TRT01A", values_from = "min_max") %>% 
  mutate(label = "Min - Max (2)",
         row_type = "VALUE")


tbl6 <- bind_rows(tbl6a, tbl6b, tbl6c) %>% 
  mutate(group_level = 0)

# combine analysis results together
tbl <- bind_table(tbl1, tbl2, tbl3, tbl4, tbl5, tbl6)

# output the analysis results
gentlg(huxme       = tbl,
       format      = "HTML",
       print.hux = FALSE,
       file        = "Table x.x.x.x",
       orientation = "portrait",
       title = "Time to First Serious Adverse Event",
       footers = c("(1) Product-limit (Kaplan-Meier) estimates.",
                   "(2) * indicates censoring",
                   "Serious adverse events are defines as (...). All p-values are exploratory.",
                   "Hazard ratios are from a stratified Cox model of serious adverse event hazard rate,
with terms for treatment groups and strata1. Ties were handled using the exact
method. Hazard ratios of Placebo Combination over Drug X are presented, an
HR < 1 denotes improvement compared to Drug X.",
"Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY"),
       colheader = c("","A: Drug X","B: Placebo","C: Combination"))  
[[1]]
  <div style='border-top :1pt solid; border-bottom :1pt solid; '>              
  <div style = "text-indent: -36px; padding-left: 36px;"> Table                
  x.x.x.x: &emsp; Time to First Serious Adverse Event</div>                    
                                       <div           <div           <div      
                                   style='borde   style='borde   style='borde  
                                   r-bottom:1pt   r-bottom:1pt   r-bottom:1pt  
                                    solid'> A:     solid'> B:     solid'> C:   
                                      Drug X        Placebo      Combination   
  <div style='text-indent:             134            134            132       
  -17.76px; padding-left:                                                      
  17.76px'> Analysis Set: Safety                                               
  Population                                                                   
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:          89 (66.4%)    123 (91.8%)     88 (66.7%)   
  -17.76px; padding-left:                                                      
  17.76px'> Number of subjects                                                 
  with serious adverse events,                                                 
  n(%)                                                                         
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:          45 (33.6%)     11 (8.2%)      44 (33.3%)   
  -17.76px; padding-left:                                                      
  17.76px'> Number of censored                                                 
  subjects, n(%)                                                               
  <div style='text-indent:          10 (7.5%)       4 (3.0%)      14 (10.6%)   
  -17.76px; padding-left:                                                      
  35.52px'> Clinical Cut Off                                                   
  <div style='text-indent:          13 (9.7%)       3 (2.2%)      16 (12.1%)   
  -17.76px; padding-left:                                                      
  35.52px'> Completion or                                                      
  Discontinuation                                                              
  <div style='text-indent:          22 (16.4%)      4 (3.0%)      14 (10.6%)   
  -17.76px; padding-left:                                                      
  35.52px'> End of AE Reporting                                                
  Period                                                                       
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:                            1.46           1.09      
  -17.76px; padding-left:                                                      
  17.76px'> Hazard ratio                                                       
  <div style='text-indent:                        (1.11, 1.92)   (0.81, 1.47)  
  -17.76px; padding-left:                                                      
  35.52px'> 95% Confidence                                                     
  Interval                                                                     
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:                           0.021          0.462      
  -17.76px; padding-left:                                                      
  17.76px'> p-value (one-sided                                                 
  stratified log rank)                                                         
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'> Time to first                                                      
  serious adverse event (1)                                                    
  <div style='text-indent:             0.39           0.37           0.26      
  -17.76px; padding-left:                                                      
  35.52px'> Median (years)                                                     
  <div style='text-indent:         (0.23, 0.60)   (0.25, 0.46)   (0.18, 0.34)  
  -17.76px; padding-left:                                                      
  35.52px'> 95% Confidence                                                     
  Interval                                                                     
  <div style='text-indent:            (0.00,         (0.01,         (0.00,     
  -17.76px; padding-left:             3.00*)         3.00*)         3.00*)     
  35.52px'> Min - Max (2)                                                      
  <div style='border-top:1pt solid;'> <br />(1) Product-limit                  
  (Kaplan-Meier) estimates.                                                    
  (2) * indicates censoring                                                    
  Serious adverse events are defines as (...). All p-values are                
  exploratory.                                                                 
  Hazard ratios are from a stratified Cox model of serious adverse             
  event hazard rate,                                                           
  with terms for treatment groups and strata1. Ties were handled               
  using the exact                                                              
  method. Hazard ratios of Placebo Combination over Drug X are                 
  presented, an                                                                
  HR < 1 denotes improvement compared to Drug X.                               
  Source: ADTTE DDMMYYYY hh:mm; Listing x.xx; SDTM package: DDMMYYYY           
                        <div style='border-bottom:1pt solid'> [table           
  x.x.x.x.html][/home/runner/work/_temp/5a83a2a5-6d01-4ccf-8f57-0c47           
                                          2be60bd2] 28JUN2024, 18:07           

Column names: label, A: Drug X, B: Placebo, C: Combination

4.3.9 tfrmt

This first code chunk cleans up the data from the models to prepare it for going into a table.

Code
library(tidyverse)
library(broom)

big_n <- surv_tbl |> 
  dplyr::select(N = n.max, TRT01A)

# Number of subjects with a serious AE 
sae_n <- surv_tbl |> # Calculated above 
  dplyr::mutate(pct = events/n.max, 
                group = "Number of subjects with serious adverse event, n (%)",
                label = "Number of subjects with serious adverse event, n (%)",
                ord1 = 1 ) |>
  dplyr::select(TRT01A, n = events, pct, group, label, ord1) |> 
  tidyr::pivot_longer(c("n", "pct"))

# Count the number of censored subjects
adtte_with_N <- adtte |>
  dplyr::left_join(big_n, by = "TRT01A")

cnsr_subjs <- adtte_with_N |> 
  dplyr::filter(CNSR == "1") 

tot_cnsr_subj <- cnsr_subjs |> 
  dplyr::group_by(TRT01A) |> 
  dplyr::reframe(
    n = n_distinct(USUBJID),
    pct = n/N
  ) |> 
  dplyr::distinct() |> 
  tidyr::pivot_longer(c("n", "pct")) |> 
  dplyr::mutate(
    group = "Number of censored subjects, n (%)",
    label = "Number of censored subjects, n (%)",
    ord1 = 2
  )

sub_cnsr_subj <- cnsr_subjs |> 
  dplyr::group_by(TRT01A, CNSDTDSC) |> 
  dplyr::reframe(
    n = n_distinct(USUBJID),
    pct = n/N
  ) |> 
  dplyr::distinct() |> 
  tidyr::pivot_longer(c("n", "pct")) |> 
  dplyr::mutate(
    group = "Number of censored subjects, n (%)",
    ord1 = 2
  ) |> 
  dplyr::rename(label = CNSDTDSC)

# Information from the CPH model 
hzr <- broom::tidy(cph, conf.int = TRUE) |> 
  mutate(across(c("estimate", "conf.low", "conf.high"), exp)) |> 
  dplyr::filter(stringr::str_detect(term, "TRT01A")) |>
  dplyr::select(term, estimate, conf.low, conf.high) |> 
  tidyr::pivot_longer(c("estimate", "conf.low", "conf.high")) |> 
  dplyr::mutate(group = "Hazard ratio",
                label = case_when(
                  name == "estimate" ~ "Hazard ratio",
                  TRUE ~ "95% confidence interval"
                ),
                TRT01A = case_when(
                  stringr::str_detect(term, "Placebo") ~ "B: Placebo",
                  stringr::str_detect(term, "Combination") ~ "C: Combination"
                ),
                ord1 = 3) |> 
  dplyr::select(-term)

# Get one-sided p-value from survival model
p_vals <- list(c("A: Drug X", "B: Placebo"), c("A: Drug X", "C: Combination")) |> 
  map_dfr(function(comparison){
    survdiff(Surv(AVAL, CNSR == 0) ~ TRT01A + STRATA1, data = adtte |> 
                        dplyr::filter(TRT01A %in% comparison)) |> 
    broom::glance() |> 
      dplyr::mutate(TRT01A = comparison[2])
  }) |> 
  dplyr::select(value = p.value, TRT01A) |> 
  dplyr::mutate(
    name = "p.value",
    group = "p-value (one-sided stratified log rank)",
    label = "p-value (one-sided stratified log rank)",
    ord1 = 5
  )


# Time to event from model 
time_to_event <- surv_tbl |> 
  dplyr::select(TRT01A, median, LCL = `0.95LCL`, UCL=`0.95UCL`) |> 
  tidyr::pivot_longer(c("median", "LCL", "UCL")) |> 
  dplyr::mutate(
    group = "Time to first serious adverse event",
    label = case_when(
      name == "median" ~ "Median (years)",
      TRUE ~ "95% confidence interval"
    ),
    ord1 = 6
  )

range <- adtte |> 
  dplyr::group_by(TRT01A) |> 
  dplyr::summarise(
    min = min(AVAL),
    max = max(AVAL)
  ) |> 
  dplyr::mutate(group = "Time to first serious adverse event",
                label = "Min, Max",
                ord1 = 6)|> 
  tidyr::pivot_longer(c("min", "max"))


model_ard <- bind_rows(
  sae_n,
  tot_cnsr_subj,
  sub_cnsr_subj,
  hzr,
  p_vals,
  time_to_event,
  range
)

We now format this information into a table.

Code
library(tfrmt)
tfrmt(
  column = TRT01A,
  group = "group",
  label = "label",
  param = "name", 
  value = "value", 
  sorting_cols = "ord1",
  body_plan = body_plan(
    frmt_structure(group_val = ".default", label_val = ".default", 
                   frmt_combine("{n} ({pct})",
                                                                                n = frmt("xx"),
                                                                                pct = frmt("xx%", transform = ~.*100))
                   
    ),
    frmt_structure(group_val = "Hazard ratio", label_val = ".default",
                   frmt_combine("[{conf.low}, {conf.high}]",
                                frmt("x.x"))),
    frmt_structure(group_val = ".default", label_val = "Hazard ratio", frmt("x.x")),
    frmt_structure(group_val = ".default", label_val = "p-value (one-sided stratified log rank)", frmt("x.xxxx")),
    frmt_structure(group_val = ".default", label_val = "Median (years)", frmt("x.xx")),
    frmt_structure(group_val = "Time to first serious adverse event", label_val = "95% confidence interval",
                   frmt_combine("[{LCL}, {UCL}]",
                                frmt("x.xx"))),
    frmt_structure(group_val = ".default", label_val = "Min, Max",
                   frmt_combine("[{min}, {max}*]",
                                frmt("x.xx")))
    
  ), 
  col_plan = col_plan(-ord1), 
  footnote_plan = footnote_plan(
    footnote_structure("Serious adverse events are defines as (...). All p-values are exploratory.
                       Hazard ratios are from a stratified Cox model of serious adverse event hazard rate, with terms for treatment groups and strata1. Ties were handled using the exact method. Hazard ratios of Placebo/ Combination over Drug X are presented, a HR < 1 denotes improvement compared to Drug X."),
    footnote_structure(group_val = "Time to first serious adverse event",
                       "Product-limit (Kaplan-Meier) estimates"), 
    footnote_structure(group_val = "Time to first serious adverse event", 
                       label_val = "Min, Max", "Minimum and maximum of event times. * Denotes censoring")
  )
) |> 
  print_to_gt(model_ard)
A: Drug X B: Placebo C: Combination
Number of subjects with serious adverse event, n (%) 89 (66%) 123 (92%) 88 (67%)
Number of censored subjects, n (%) 45 (34%) 11 ( 8%) 44 (33%)
  Clinical Cut Off 10 ( 7%)  4 ( 3%) 14 (11%)
  Completion or Discontinuation 13 (10%)  3 ( 2%) 16 (12%)
  End of AE Reporting Period 22 (16%)  4 ( 3%) 14 (11%)
Hazard ratio 1.5 1.1
  95% confidence interval [1.1, 1.9] [0.8, 1.5]
p-value (one-sided stratified log rank) 0.0416 0.9239
Time to first serious adverse event1


  Median (years) 0.39 0.37 0.26
  95% confidence interval [0.23, 0.60] [0.25, 0.46] [0.18, 0.34]
  Min, Max2 [0.00, 3.00*] [0.01, 3.00*] [0.00, 3.00*]
Serious adverse events are defines as (...). All p-values are exploratory. Hazard ratios are from a stratified Cox model of serious adverse event hazard rate, with terms for treatment groups and strata1. Ties were handled using the exact method. Hazard ratios of Placebo/ Combination over Drug X are presented, a HR < 1 denotes improvement compared to Drug X.
1 Product-limit (Kaplan-Meier) estimates
2 Minimum and maximum of event times. * Denotes censoring

4.4 Concomitant Medications

4.4.1 rtables

Code
resetSession()

library(rtables)
data("cadcm", package = "random.cdisc.data")
data("cadsl", package = "random.cdisc.data")

one_count_pct_gen <- function(label = NULL) {
    function(x, .N_col) {
       ret <- rcell(length(unique(x)) * c(1, 1/.N_col),
                    format = "xx (xx.x%)")
       if(!is.null(label))
           obj_label(ret) <- label
       ret
    }
}

lyt <- basic_table(title = "Conmed Example",
                   subtitles = "Uses the adcm dataset from random.cdisc.data",
                   show_colcounts = TRUE) |>
    split_cols_by("ARM") |>
    analyze("USUBJID", afun = one_count_pct_gen("At Least One Concomittant Med")) |>
    split_rows_by("CMCLAS", split_fun = trim_levels_in_group("CMTRT")) |>
    analyze("CMTRT", afun = function(df, .N_col) {
        cmtrtvec <- df$CMTRT
        spl_usubj <- split(df$USUBJID, cmtrtvec)
        fn <- one_count_pct_gen()
        cells <- lapply(spl_usubj, fn, .N_col = .N_col)
        names(cells) <- names(spl_usubj)
        in_rows(.list = cells)
    })

build_table(lyt, cadcm, alt_counts_df = cadsl)
Conmed Example
Uses the adcm dataset from random.cdisc.data

——————————————————————————————————————————————————————————————————————————
                                 A: Drug X    B: Placebo    C: Combination
                                  (N=134)       (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————
At Least One Concomittant Med   122 (91.0%)   123 (91.8%)    120 (90.9%)  
medcl A                                                                   
  A_1/3                         54 (40.3%)    49 (36.6%)      69 (52.3%)  
  A_2/3                         53 (39.6%)    50 (37.3%)      56 (42.4%)  
  A_3/3                         45 (33.6%)    54 (40.3%)      48 (36.4%)  
medcl B                                                                   
  B_1/4                         52 (38.8%)    57 (42.5%)      59 (44.7%)  
  B_2/4                         52 (38.8%)    55 (41.0%)      56 (42.4%)  
  B_3/4                         47 (35.1%)    47 (35.1%)      52 (39.4%)  
  B_4/4                         50 (37.3%)    45 (33.6%)      55 (41.7%)  
medcl C                                                                   
  C_1/2                         51 (38.1%)    50 (37.3%)      56 (42.4%)  
  C_2/2                         52 (38.8%)    58 (43.3%)      60 (45.5%)  

4.4.2 tern (+ rtables)

Code
library(tern)
lyt <- basic_table(show_colcounts = TRUE) |>
  split_cols_by(var = "ARM") |>
  analyze_num_patients(vars = "USUBJID",
                       .stats = "unique",
                       .labels = "At Least One Concomittant Med") |>
  split_rows_by("CMCLAS",
                split_fun = drop_split_levels) |>
  count_occurrences(vars = "CMDECOD")

build_table(lyt = lyt, df = cadcm, alt_counts_df = cadsl)
                                 A: Drug X    B: Placebo    C: Combination
                                  (N=134)       (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————
At Least One Concomittant Med   122 (91.0%)   123 (91.8%)    120 (90.9%)  
medcl A                                                                   
  medname A_1/3                 54 (40.3%)    49 (36.6%)      69 (52.3%)  
  medname A_2/3                 53 (39.6%)    50 (37.3%)      56 (42.4%)  
  medname A_3/3                 45 (33.6%)    54 (40.3%)      48 (36.4%)  
medcl B                                                                   
  medname B_1/4                 52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_2/4                 52 (38.8%)    55 (41.0%)      56 (42.4%)  
  medname B_3/4                 47 (35.1%)    47 (35.1%)      52 (39.4%)  
  medname B_4/4                 50 (37.3%)    45 (33.6%)      55 (41.7%)  
medcl C                                                                   
  medname C_1/2                 51 (38.1%)    50 (37.3%)      56 (42.4%)  
  medname C_2/2                 52 (38.8%)    58 (43.3%)      60 (45.5%)  

4.4.3 flextable

This is again a situation where the code required to create a flextable directly requires too much data preparation. In the following example, we convert the ‘rtables’ object to a flextable using the as_flextable() method and then we change its aspect.

Code
library(flextable)

tt_to_flextable(build_table(lyt, cadcm, alt_counts_df = cadsl)) |> 
  theme_booktabs() |> 
  font(fontname = "Open Sans") |> 
  bold(i = ~ V2 %in% "", j = 1, bold = TRUE) |> 
  bold(i = 1, j = 1, bold = TRUE) |> 
  align(j = 2:4, align = "center", part = "all") |> 
  set_table_properties(layout = "fixed") |> 
  autofit() |> 
  mk_par(i = 1, j = 1, part = "header",
         as_paragraph(as_chunk("Conmed Example", props = fp_text_default(font.size = 14))))

Conmed Example

A: Drug X (N=134)

B: Placebo (N=134)

C: Combination (N=132)

At Least One Concomittant Med

122 (91.0%)

123 (91.8%)

120 (90.9%)

medcl A

medname A_1/3

54 (40.3%)

49 (36.6%)

69 (52.3%)

medname A_2/3

53 (39.6%)

50 (37.3%)

56 (42.4%)

medname A_3/3

45 (33.6%)

54 (40.3%)

48 (36.4%)

medcl B

medname B_1/4

52 (38.8%)

57 (42.5%)

59 (44.7%)

medname B_2/4

52 (38.8%)

55 (41.0%)

56 (42.4%)

medname B_3/4

47 (35.1%)

47 (35.1%)

52 (39.4%)

medname B_4/4

50 (37.3%)

45 (33.6%)

55 (41.7%)

medcl C

medname C_1/2

51 (38.1%)

50 (37.3%)

56 (42.4%)

medname C_2/2

52 (38.8%)

58 (43.3%)

60 (45.5%)

4.4.4 gt

Code
resetSession()

library(dplyr)
library(tidyr)
library(gt)

data("cadcm", package = "random.cdisc.data")
data("cadsl", package = "random.cdisc.data")


cmdecod_levels <- c("Number of sujects with any concomitant medication", levels(cadcm$CMDECOD))
cmclas_levels <- c(NA, levels(cadcm$CMCLAS))

adcm <- cadcm |> 
  dplyr::select(CMDECOD, CMCLAS, TRT01A) |> 
  dplyr::mutate(
    CMDECOD = factor(CMDECOD, levels = cmdecod_levels),
    CMCLAS = factor(CMCLAS, levels = cmclas_levels)
    )

ct_cm <- cadcm |> 
  dplyr::summarize(
    n = dplyr::n_distinct(USUBJID), 
    .by = TRT01A
    ) |> 
  dplyr::left_join(count(cadsl, TRT01A, name = "nall"), by = "TRT01A") |> 
  dplyr::mutate(
    pct = n / nall, nall = NULL,
    CMDECOD = factor("Number of sujects with any concomitant medication", levels = cmdecod_levels)
    )

ct_adcm <- cadcm |> 
  dplyr::summarize(
    n = dplyr::n_distinct(USUBJID), 
    .by = c(TRT01A, CMCLAS, CMDECOD)
    ) |> 
  dplyr::left_join(count(cadsl, TRT01A, name = "nall"), by = "TRT01A") |> 
  dplyr::mutate(pct = n / nall, nall = NULL)

gt_adcm <- dplyr::bind_rows(ct_cm, ct_adcm) |>  
  tidyr::pivot_wider(id_cols = c(CMCLAS, CMDECOD), names_from = TRT01A, values_from = c(n, pct)) 


trt_n <- cadsl |> 
  dplyr::filter(SAFFL == "Y") |> 
  dplyr::summarize(
    n = sprintf("%s  \n(N=%i)", unique(TRT01A), dplyr::n()), 
    .by = TRT01A
    ) 

header_n <- as.list(trt_n$n) 
names(header_n) <- paste("n", dplyr::pull(trt_n, TRT01A), sep = "_")


gt_adcm |> 
  gt(rowname_col = "CMDECOD") |> 
  tab_header(
    title = "Conmed Example",
    subtitle = md("Uses the *adcm* dataset from **random.cdisc.data**")
  ) |> 
  opt_align_table_header(align = "left") |> 
  fmt_percent(columns = dplyr::starts_with("pct_"), decimals = 1) |> 
  cols_merge_n_pct(col_n = "n_A: Drug X", col_pct = "pct_A: Drug X") |> 
  cols_merge_n_pct(col_n = "n_B: Placebo", col_pct = "pct_B: Placebo") |> 
  cols_merge_n_pct(col_n = "n_C: Combination", col_pct = "pct_C: Combination") |> 
  tab_row_group(
    label = "medcl A",
    rows = CMCLAS == "medcl A"
  ) |>
  tab_row_group(
    label = "medcl B",
    rows = CMCLAS == "medcl B"
  ) |>
  tab_row_group(
    label = "medcl C",
    rows = CMCLAS == "medcl C"
  ) |>
  row_group_order(
    groups = c(NA, paste("medcl", LETTERS[1:2])) 
  ) |> 
  cols_hide(CMCLAS) |> 
  cols_label(
    .list = header_n,
    .fn = md
  ) |> 
  cols_width(
    1 ~ px(500),
    everything() ~ px(150)
  ) |> 
  cols_align(
    align = "center",
    columns = everything()
  ) |> 
  cols_align(
    align = "left",
    columns = 1
  ) 
Conmed Example
Uses the adcm dataset from random.cdisc.data
A: Drug X
(N=134)
C: Combination
(N=132)
B: Placebo
(N=134)
Number of sujects with any concomitant medication 122 (91.0%) 120 (90.9%) 123 (91.8%)
medcl A
medname A_2/3 53 (39.6%) 56 (42.4%) 50 (37.3%)
medname A_3/3 45 (33.6%) 48 (36.4%) 54 (40.3%)
medname A_1/3 54 (40.3%) 69 (52.3%) 49 (36.6%)
medcl B
medname B_1/4 52 (38.8%) 59 (44.7%) 57 (42.5%)
medname B_4/4 50 (37.3%) 55 (41.7%) 45 (33.6%)
medname B_2/4 52 (38.8%) 56 (42.4%) 55 (41.0%)
medname B_3/4 47 (35.1%) 52 (39.4%) 47 (35.1%)
medcl C
medname C_1/2 51 (38.1%) 56 (42.4%) 50 (37.3%)
medname C_2/2 52 (38.8%) 60 (45.5%) 58 (43.3%)

4.4.5 tables

Code
resetSession()

data("cadcm", package = "random.cdisc.data")

library(tables)
table_options(doCSS = TRUE)

subject_counts <- table(adsl$ARM)

countpercentid <- function(num, ARM) {
  n <- length(unique(num))
  if (n == 0) pct <- 0
  else        pct <- 100*n/subject_counts[ARM[1]]
  sprintf("%d (%.2f%%)", 
          length(unique(num)), 
          pct)
}

count <- function(x) sprintf("(N=%d)", length(x))

heading <- tabular(Heading("")*1*Heading("")*count ~ 
                   Heading()*ARM, 
                   data = adsl)

body <- tabular( (Heading("Any concomitant medication")*1*Heading("")*1 + 
                  Heading()*CMCLAS*
                    Heading()*CMDECOD*DropEmpty(which = "row"))*
                 Heading()*countpercentid*Arguments(ARM = TRT01A)*
                   Heading()*USUBJID ~
                 Heading()*TRT01A, 
                 data = cadcm)

useGroupLabels(rbind(heading, body), indent = "&emsp;")
A: Drug X B: Placebo C: Combination
(N=134) (N=134) (N=132)
Any concomitant medication 122 (91.04%) 123 (91.79%) 120 (90.91%)
medcl A      
 medname A_1/3 54 (40.30%) 49 (36.57%) 69 (52.27%)
 medname A_2/3 53 (39.55%) 50 (37.31%) 56 (42.42%)
 medname A_3/3 45 (33.58%) 54 (40.30%) 48 (36.36%)
medcl B      
 medname B_1/4 52 (38.81%) 57 (42.54%) 59 (44.70%)
 medname B_2/4 52 (38.81%) 55 (41.04%) 56 (42.42%)
 medname B_3/4 47 (35.07%) 47 (35.07%) 52 (39.39%)
 medname B_4/4 50 (37.31%) 45 (33.58%) 55 (41.67%)
medcl C      
 medname C_1/2 51 (38.06%) 50 (37.31%) 56 (42.42%)
 medname C_2/2 52 (38.81%) 58 (43.28%) 60 (45.45%)

4.4.6 tidytlg

Code
resetSession()
library(dplyr)
library(tidytlg)

data("cadcm", package = "random.cdisc.data")
data("cadsl", package = "random.cdisc.data")

adsl <- cadsl 

adcm <- cadcm %>% 
  filter(SAFFL == "Y") %>% 
  mutate(CMFL = "Y")

# Create analysis population counts
tbl1 <- freq(adsl,
             rowvar = "SAFFL",
             colvar = "ARM",
             statlist = statlist("n"),
             rowtext = "Analysis Set: Safety Population",
             subset = SAFFL == "Y")

# Create counts (percentages) for patients with any ConMed
tbl2 <- freq(adcm,
             denom_df = adsl,
             rowvar = "CMFL",
             colvar = "ARM",
             statlist = statlist("n (x.x%)"),
             rowtext = "Number of subjects with any concomitant medication",
             subset = CMFL == "Y")

# Create counts (percentages) by CMCLAS and CMDECOD
tbl3 <- nested_freq(adcm,
                    denom_df = adsl,
                    rowvar = "CMCLAS*CMDECOD",
                    colvar = "ARM",
                    statlist = statlist("n (x.x%)"))

# combine analysis results together
tbl <- bind_table(tbl1, tbl2, tbl3) %>% 
  select(-CMCLAS)

# output the analysis results
gentlg(huxme       = tbl,
       format      = "HTML",
       print.hux = FALSE,
       file        = "Table x.x.x.x",
       orientation = "portrait",
       title = "Conmed Example Uses the ‘adcm’ dataset from ‘random.cdisc.data’",
       colheader = c("","A: Drug X","B: Placebo","C: Combination"))
[[1]]
  <div style='border-top :1pt solid; border-bottom :1pt solid; '>              
  <div style = "text-indent: -36px; padding-left: 36px;"> Table                
  x.x.x.x: &emsp; Conmed Example Uses the ‘adcm’ dataset from                  
  ‘random.cdisc.data’</div>                                                    
                                       <div           <div           <div      
                                   style='borde   style='borde   style='borde  
                                   r-bottom:1pt   r-bottom:1pt   r-bottom:1pt  
                                    solid'> A:     solid'> B:     solid'> C:   
                                      Drug X        Placebo      Combination   
  <div style='text-indent:             134            134            132       
  -17.76px; padding-left:                                                      
  17.76px'> Analysis Set: Safety                                               
  Population                                                                   
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:         122 (91.0%)    123 (91.8%)    120 (90.9%)   
  -17.76px; padding-left:                                                      
  17.76px'> Number of subjects                                                 
  with any concomitant                                                         
  medication                                                                   
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:          97 (72.4%)     98 (73.1%)    102 (77.3%)   
  -17.76px; padding-left:                                                      
  17.76px'> medcl A                                                            
  <div style='text-indent:          54 (40.3%)     49 (36.6%)     69 (52.3%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname A_1/3                                                      
  <div style='text-indent:          53 (39.6%)     50 (37.3%)     56 (42.4%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname A_2/3                                                      
  <div style='text-indent:          45 (33.6%)     54 (40.3%)     48 (36.4%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname A_3/3                                                      
  <div style='text-indent:         102 (76.1%)    101 (75.4%)    108 (81.8%)   
  -17.76px; padding-left:                                                      
  17.76px'> medcl B                                                            
  <div style='text-indent:          52 (38.8%)     57 (42.5%)     59 (44.7%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname B_1/4                                                      
  <div style='text-indent:          52 (38.8%)     55 (41.0%)     56 (42.4%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname B_2/4                                                      
  <div style='text-indent:          47 (35.1%)     47 (35.1%)     52 (39.4%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname B_3/4                                                      
  <div style='text-indent:          50 (37.3%)     45 (33.6%)     55 (41.7%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname B_4/4                                                      
  <div style='text-indent:          82 (61.2%)     84 (62.7%)     89 (67.4%)   
  -17.76px; padding-left:                                                      
  17.76px'> medcl C                                                            
  <div style='text-indent:          51 (38.1%)     50 (37.3%)     56 (42.4%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname C_1/2                                                      
  <div style='text-indent:          52 (38.8%)     58 (43.3%)     60 (45.5%)   
  -17.76px; padding-left:                                                      
  35.52px'> medname C_2/2                                                      
                        <div style='border-bottom:1pt solid'> [table           
  x.x.x.x.html][/home/runner/work/_temp/5a83a2a5-6d01-4ccf-8f57-0c47           
                                          2be60bd2] 28JUN2024, 18:07           

Column names: label, A: Drug X, B: Placebo, C: Combination

4.4.7 tfrmt

Rather than starting with an ADaM, tfrmt assumes users will start with an ARD (Analysis Results Dataset), because of this, making this table will be split into two parts, first to make the ARD and second to format the table.

Code
resetSession()
library(tidyverse)
library(tfrmt)

## Create ARD 
data("cadcm", package = "random.cdisc.data")
data("cadsl", package = "random.cdisc.data")

big_n <- cadsl |> 
  dplyr::group_by(ARM) |> 
  dplyr::summarize(
    N = dplyr::n_distinct(USUBJID)
  ) 

adcm_with_N <- cadcm |> 
  left_join(big_n, by= "ARM")

overall <- adcm_with_N |> 
  dplyr::group_by(ARM) |>
  dplyr::reframe(
    n_subj = n_distinct(USUBJID),
    pct_subj = n_subj/N
  ) |>
  dplyr::distinct() |>
  dplyr::mutate(CMCLAS = "At Least One Concomittant Med",
         CMDECOD = CMCLAS)

med_lvl <- adcm_with_N |> 
  dplyr::group_by(ARM,CMDECOD, CMCLAS) |>
  dplyr::reframe(
    n_subj = dplyr::n_distinct(USUBJID),
    pct_subj = n_subj/N
  ) |>
  distinct() 

label_N <- big_n |> 
  dplyr::rename(value = N) |> 
  dplyr::mutate(name = "header_n")


cm_ard <- bind_rows(overall, med_lvl) |>
  pivot_longer(ends_with("subj")) |> 
  bind_rows(label_N)

## Format Table 
tfrmt(
  column = ARM,
  group = c("CMCLAS"),
  param = name,
  value = value,
  label = CMDECOD, 
) |>
  # Then we cam combine it with an n percent template 
tfrmt_n_pct(n = "n_subj",
            pct = "pct_subj",
  pct_frmt_when = frmt_when("==1" ~ "", 
                            ">.99" ~ "(>99%)", 
                            "==0" ~ "", 
                            "<.01" ~ "(<1%)", 
                            "TRUE" ~ frmt("(xx.x%)", transform = ~.*100))
  ) |>
  #Finally we are going to add some additional formatting
  tfrmt(
    big_n = big_n_structure("header_n"),
    # Aligning on decimal places and spaces
    col_style_plan = col_style_plan(
      col_style_structure(col = matches("[A-Z]:.*"),
                          align = c(".", " "))
    )
  ) |> 
  print_to_gt(cm_ard)
A: Drug X N = 134 B: Placebo N = 134 C: Combination N = 132
At Least One Concomittant Med 122 (91.0%) 123 (91.8%) 120 (90.9%)
medcl A


  medname A_1/3  54 (40.3%)  49 (36.6%)  69 (52.3%)
  medname A_2/3  53 (39.6%)  50 (37.3%)  56 (42.4%)
  medname A_3/3  45 (33.6%)  54 (40.3%)  48 (36.4%)
medcl B


  medname B_1/4  52 (38.8%)  57 (42.5%)  59 (44.7%)
  medname B_2/4  52 (38.8%)  55 (41.0%)  56 (42.4%)
  medname B_3/4  47 (35.1%)  47 (35.1%)  52 (39.4%)
  medname B_4/4  50 (37.3%)  45 (33.6%)  55 (41.7%)
medcl C


  medname C_1/2  51 (38.1%)  50 (37.3%)  56 (42.4%)
  medname C_2/2  52 (38.8%)  58 (43.3%)  60 (45.5%)

4.5 Disposition

4.5.1 rtables

Code
resetSession()

library(dplyr)
library(rtables)

data("cadsl", package = "random.cdisc.data")
adsl <- cadsl |>
  select(USUBJID, TRT01A, EOSSTT, DCSREAS, DTHCAUS)

top_afun <- function(x, .N_col) {
    in_rows(Completed = rcell(sum(x=="COMPLETED") * c(1, 1/.N_col), format = c("xx (xx.x%)")),
            Ongoing = rcell(sum(x=="ONGOING") * c(1, 1/.N_col), format = c("xx (xx.x%)")))
}

count_pct_afun <- function(x, .N_col) {
    tbl <- table(x)
    lst <- lapply(tbl, function(xi) rcell(xi * c(1, 1/.N_col), format = c("xx (xx.x%)")))
    in_rows(.list = lst, .names = names(tbl))
}
            
lyt <- basic_table(show_colcounts = TRUE) |>
  split_cols_by("TRT01A") |>
  analyze("EOSSTT", top_afun) |>
  split_rows_by("EOSSTT", split_fun = keep_split_levels("DISCONTINUED")) |>
  analyze("DCSREAS", count_pct_afun) |>
  split_rows_by("DCSREAS", split_fun = keep_split_levels("DEATH")) |>
  analyze("DTHCAUS", count_pct_afun)

build_table(lyt, adsl)
                                  A: Drug X    B: Placebo   C: Combination
                                   (N=134)      (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————————
Completed                         68 (50.7%)   66 (49.3%)     73 (55.3%)  
Ongoing                           24 (17.9%)   28 (20.9%)     21 (15.9%)  
DISCONTINUED                                                              
  ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)   
  DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)  
  LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)   
  PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)   
  PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)       4 (3.0%)   
  WITHDRAWAL BY PARENT/GUARDIAN    4 (3.0%)     2 (1.5%)       1 (0.8%)   
  WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)   
DEATH                                                                     
  ADVERSE EVENT                    9 (6.7%)     7 (5.2%)      10 (7.6%)   
  DISEASE PROGRESSION              8 (6.0%)     6 (4.5%)       6 (4.5%)   
  LOST TO FOLLOW UP                2 (1.5%)     2 (1.5%)       2 (1.5%)   
  MISSING                          2 (1.5%)     3 (2.2%)       2 (1.5%)   
  Post-study reporting of death    1 (0.7%)     2 (1.5%)       1 (0.8%)   
  SUICIDE                          2 (1.5%)     2 (1.5%)       1 (0.8%)   
  UNKNOWN                          1 (0.7%)     1 (0.7%)       0 (0.0%)   

4.5.2 tern (+ rtables)

tern makes slightly different formatting choices (2 decimals for the percents of completed and ongoing study counts, and not displaying the percent when a cell count is 0), but we can see the table structure and cell values are the same.

Code
library(tern)
lyt <- basic_table(show_colcounts = TRUE) |>
  split_cols_by("TRT01A") |>
  count_values("EOSSTT",
               values = "COMPLETED",
               table_names = "Completed",
               .labels = c(count_fraction = "Completed Study")) |>
  count_values("EOSSTT",
               values = "ONGOING",
               table_names = "Ongoing",
               .labels = c(count_fraction = "Ongoing Study")) |>
  split_rows_by("EOSSTT",
                split_fun = keep_split_levels("DISCONTINUED")) |>
  analyze_vars("DCSREAS",
                 .stats = "count_fraction",
                 denom = "N_col") |>
  split_rows_by("DCSREAS",
                split_fun = keep_split_levels("DEATH")) |>
  analyze_vars("DTHCAUS",
                 .stats = "count_fraction",
                 denom = "N_col")

build_table(lyt = lyt, df = adsl)
                                   A: Drug X    B: Placebo    C: Combination
                                    (N=134)       (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————————————
Completed Study                   68 (50.75%)   66 (49.25%)    73 (55.30%)  
Ongoing Study                     24 (17.91%)   28 (20.90%)    21 (15.91%)  
DISCONTINUED                                                                
  ADVERSE EVENT                    3 (2.2%)      6 (4.5%)        5 (3.8%)   
  DEATH                           25 (18.7%)    23 (17.2%)      22 (16.7%)  
  LACK OF EFFICACY                 2 (1.5%)      2 (1.5%)        3 (2.3%)   
  PHYSICIAN DECISION               2 (1.5%)      3 (2.2%)        2 (1.5%)   
  PROTOCOL VIOLATION               5 (3.7%)      3 (2.2%)         4 (3%)    
  WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)       2 (1.5%)        1 (0.8%)   
  WITHDRAWAL BY SUBJECT            1 (0.7%)      1 (0.7%)        1 (0.8%)   
DEATH                                                                       
  ADVERSE EVENT                    9 (6.7%)      7 (5.2%)       10 (7.6%)   
  DISEASE PROGRESSION               8 (6%)       6 (4.5%)        6 (4.5%)   
  LOST TO FOLLOW UP                2 (1.5%)      2 (1.5%)        2 (1.5%)   
  MISSING                          2 (1.5%)      3 (2.2%)        2 (1.5%)   
  Post-study reporting of death    1 (0.7%)      2 (1.5%)        1 (0.8%)   
  SUICIDE                          2 (1.5%)      2 (1.5%)        1 (0.8%)   
  UNKNOWN                          1 (0.7%)      1 (0.7%)           0       

4.5.3 gt

Code
resetSession()

library(tidyverse)
library(gt)

adsl_tot <- cadsl |> 
  dplyr::summarize(
    NTOT = dplyr::n(),
    NTOTLBL = sprintf("%s  \n(N=%i)", unique(TRT01A), dplyr::n()),
    .by = TRT01A
  )

header_n <- as.list(adsl_tot$NTOTLBL)
names(header_n) <- paste0("n_", adsl_tot$TRT01A)

disp_df <- merge(cadsl, adsl_tot, by = "TRT01A") |> 
  dplyr::mutate(
    EOSSTT = factor(EOSSTT, levels = c("COMPLETED", "ONGOING", "DISCONTINUED"))
  )

disc_status <- disp_df |> 
  dplyr::filter(EOSSTT != "DISCONTINUED") |> 
  dplyr::summarize(
    n = dplyr::n(),
    pct = dplyr::n()/mean(NTOT),
    .by = c(TRT01A, EOSSTT)
  ) |> 
  tidyr::pivot_wider(id_cols = EOSSTT, names_from = TRT01A, values_from = c(n, pct)) |> 
  dplyr::arrange(EOSSTT)

disc_reason <- disp_df |> 
  dplyr::filter(EOSSTT == "DISCONTINUED") |> 
  dplyr::summarize(
    n = dplyr::n(),
    pct = dplyr::n()/mean(NTOT),
    .by = c(TRT01A, EOSSTT, DCSREAS)
  ) |> 
  tidyr::pivot_wider(id_cols = c(EOSSTT, DCSREAS), names_from = TRT01A, values_from = c(n, pct)) |> 
  dplyr::arrange(EOSSTT, DCSREAS)

disc_death <- disp_df |> 
  dplyr::filter(DCSREAS == "DEATH") |> 
  dplyr::mutate(
    EOSSTT = "DEATH",
    DCSREAS = DTHCAUS
    ) |> 
  dplyr::summarize(
    n = dplyr::n(),
    pct = dplyr::n()/mean(NTOT),
    .by = c(TRT01A, EOSSTT, DCSREAS)
  ) |> 
  tidyr::pivot_wider(id_cols = c(EOSSTT, DCSREAS), names_from = TRT01A, values_from = c(n, pct)) |> 
  dplyr::arrange(EOSSTT, DCSREAS)

gt_disp <- dplyr::bind_rows(disc_status, disc_reason, disc_death) 

gt_disp |> 
  gt(rowname_col = "DCSREAS") |> 
  tab_row_group(
    label = "Discontinued",
    rows = EOSSTT == "DISCONTINUED"
  ) |> 
  tab_row_group(
    label = "Death",
    rows = EOSSTT == "DEATH"
  ) |> 
  row_group_order(
    groups = c(NA, "Discontinued", "Death") 
  ) |> 
  fmt_integer(
    columns = starts_with("n_")
  ) |> 
  fmt_percent(
    columns = starts_with("pct_"),
    decimals = 2
  ) |> 
  cols_merge_n_pct(col_n = "n_A: Drug X", col_pct = "pct_A: Drug X") |> 
  cols_merge_n_pct(col_n = "n_B: Placebo", col_pct = "pct_B: Placebo") |> 
  cols_merge_n_pct(col_n = "n_C: Combination", col_pct = "pct_C: Combination") |> 
  cols_merge(
    columns = c("DCSREAS", "EOSSTT"),
    rows = EOSSTT %in% c("COMPLETED", "ONGOING"),
    pattern = "<<{1}>><<{2}>>"
  ) |> 
  sub_missing(
    columns = starts_with("n_"),
    missing_text = "0"
  ) |> 
  text_transform(
    locations = list(cells_body(), cells_stub()),
    fn = stringr::str_to_title
  ) |> 
  cols_align(
    align = "left",
    columns = "DCSREAS"
  ) |> 
  cols_align(
    align = "center",
    columns = starts_with("n_")
  ) |>  
  cols_label(
    .list = header_n,
    .fn = md
  )  |> 
  tab_stub_indent(
    rows = 3:16,
    indent = 5
  ) |> 
  cols_width(
    1 ~ px(200)
  )
A: Drug X
(N=134)
B: Placebo
(N=134)
C: Combination
(N=132)
Completed 68 (50.75%) 66 (49.25%) 73 (55.30%)
Ongoing 24 (17.91%) 28 (20.90%) 21 (15.91%)
Discontinued
Adverse Event 3 (2.24%) 6 (4.48%) 5 (3.79%)
Death 25 (18.66%) 23 (17.16%) 22 (16.67%)
Lack Of Efficacy 2 (1.49%) 2 (1.49%) 3 (2.27%)
Physician Decision 2 (1.49%) 3 (2.24%) 2 (1.52%)
Protocol Violation 5 (3.73%) 3 (2.24%) 4 (3.03%)
Withdrawal By Parent/Guardian 4 (2.99%) 2 (1.49%) 1 (0.76%)
Withdrawal By Subject 1 (0.75%) 1 (0.75%) 1 (0.76%)
Death
Adverse Event 9 (6.72%) 7 (5.22%) 10 (7.58%)
Disease Progression 8 (5.97%) 6 (4.48%) 6 (4.55%)
Lost To Follow Up 2 (1.49%) 2 (1.49%) 2 (1.52%)
Missing 2 (1.49%) 3 (2.24%) 2 (1.52%)
Post-Study Reporting Of Death 1 (0.75%) 2 (1.49%) 1 (0.76%)
Suicide 2 (1.49%) 2 (1.49%) 1 (0.76%)
Unknown 1 (0.75%) 1 (0.75%) 0

4.5.4 flextable

Code
resetSession()

library(survival)
library(tidyverse)
library(flextable)
library(glue)

adsl <- cadsl |>
  select(USUBJID, TRT01A, EOSSTT, DCSREAS)

# data parts calculations
part_header <- adsl |> count(TRT01A, name = "n_part")

part_completed <- adsl |> filter(EOSSTT %in% "COMPLETED") |> 
  mutate(DCSREAS = "") |>
  count(TRT01A, EOSSTT, DCSREAS)

part_ongoing <- adsl |> filter(EOSSTT %in% "ONGOING") |> 
  mutate(DCSREAS = "") |>
  count(TRT01A, EOSSTT, DCSREAS)

part_discontinued <- adsl |> 
  filter(EOSSTT %in% "DISCONTINUED") |> 
  count(TRT01A, EOSSTT, DCSREAS)

part_death <- cadsl |> 
  filter(EOSSTT %in% "DISCONTINUED", DCSREAS %in% "DEATH") |> 
  count(TRT01A, EOSSTT, DTHCAUS) |> 
  mutate(DTHCAUS = paste0("\t", DTHCAUS)) |> 
  rename(DCSREAS = DTHCAUS)

DCSREAS_LEV <- c(
  "", "ADVERSE EVENT", "DEATH", 
  part_death$DCSREAS, levels(part_discontinued$DCSREAS)) |> 
  unique()
EOSSTT_LEV <- c("COMPLETED", "ONGOING", "DISCONTINUED")

dat <- bind_rows(
  part_completed, 
  part_ongoing, 
  part_discontinued, 
  part_death) |> 
  inner_join(part_header, by = "TRT01A") |> 
  mutate(percent = n / n_part, n_part = NULL,
         DCSREAS = factor(DCSREAS, levels = DCSREAS_LEV),
         EOSSTT = factor(EOSSTT, levels = EOSSTT_LEV)
  )

# Now the flextable creation with help of `tabulator()`. 

tab <- tabulator(
  dat,
  rows = c("EOSSTT", "DCSREAS"),
  columns = "TRT01A",
  `content_cell` = as_paragraph(fmt_n_percent(n, percent))
)
ft <- as_flextable(tab, spread_first_col = TRUE, 
                   columns_alignment = "center" )

TRT_COUNTS <- setNames(part_header$n_part, part_header$TRT01A)
for (TRT_COD in names(TRT_COUNTS)) {
  ft <- append_chunks(x = ft, part = "header", i = 1,
                      j = tabulator_colnames(tab, columns = "content_cell", TRT01A %in% !!TRT_COD),
                      as_chunk(TRT_COUNTS[TRT_COD], formatter = function(n) sprintf("\n(N=%.0f)", n)))
}

ft <- labelizor(ft, j = "DCSREAS", part = "all", labels = function(x) tools::toTitleCase(tolower(x))) |> 
  labelizor(labels = c(Dcsreas = ""), j = "DCSREAS", part = "header") |> 
  align(i = ~!is.na(EOSSTT) | seq_along(EOSSTT) == 1, j = 1, align = "left") |> 
  prepend_chunks(i = ~is.na(EOSSTT), j = "DCSREAS", as_chunk("\t")) |> 
  autofit()
ft

A: Drug X
(N=134)

B: Placebo
(N=134)

C: Combination
(N=132)

Completed

68 (50.7%)

66 (49.3%)

73 (55.3%)

Ongoing

24 (17.9%)

28 (20.9%)

21 (15.9%)

Discontinued

Adverse Event

3 (2.2%)

6 (4.5%)

5 (3.8%)

Death

25 (18.7%)

23 (17.2%)

22 (16.7%)

  Adverse Event

9 (6.7%)

7 (5.2%)

10 (7.6%)

  Disease Progression

8 (6.0%)

6 (4.5%)

6 (4.5%)

  Lost to Follow Up

2 (1.5%)

2 (1.5%)

2 (1.5%)

  Missing

2 (1.5%)

3 (2.2%)

2 (1.5%)

  Post-Study Reporting of Death

1 (0.7%)

2 (1.5%)

1 (0.8%)

  Suicide

2 (1.5%)

2 (1.5%)

1 (0.8%)

  Unknown

1 (0.7%)

1 (0.7%)

Lack of Efficacy

2 (1.5%)

2 (1.5%)

3 (2.3%)

Physician Decision

2 (1.5%)

3 (2.2%)

2 (1.5%)

Protocol Violation

5 (3.7%)

3 (2.2%)

4 (3.0%)

Withdrawal by Parent/Guardian

4 (3.0%)

2 (1.5%)

1 (0.8%)

Withdrawal by Subject

1 (0.7%)

1 (0.7%)

1 (0.8%)

4.5.5 tables

Code
resetSession()

adsl <- cadsl

# Change the labels to title case

levels(adsl$EOSSTT)  <- tools::toTitleCase(tolower(levels(adsl$EOSSTT)))
levels(adsl$DCSREAS) <- tools::toTitleCase(tolower(levels(adsl$DCSREAS)))
levels(adsl$DTHCAUS) <- tools::toTitleCase(tolower(levels(adsl$DTHCAUS)))

library(tables)

subject_counts <- table(adsl$ARM)

countpercentid <- function(num, ARM) {
  n <- length(unique(num))
  if (n == 0) pct <- 0
  else        pct <- 100*n/subject_counts[ARM[1]]
  sprintf("%d (%.2f%%)", 
          length(unique(num)), 
          pct)
}

count <- function(x) sprintf("(N=%d)", length(x))

heading <- tabular(Heading("")*1*Heading("")*count  ~
             Heading()*TRT01A, data = adsl)

part1 <- tabular( Heading("")*EOSSTT*DropEmpty(which = "row")*
                    Heading("")*1*
                    Heading()*countpercentid*Arguments(ARM = TRT01A)*
                    Heading()*USUBJID ~
                  Heading()*TRT01A, 
                  data = subset(adsl, EOSSTT != "Discontinued"))

part2 <- tabular( Heading("")*EOSSTT*
                    Heading("")*DCSREAS*DropEmpty(which = "row")*
                    Heading()*countpercentid*Arguments(ARM = TRT01A)*
                    Heading()*USUBJID ~
                  Heading()*TRT01A, 
                  data = subset(adsl, EOSSTT == "Discontinued" &
                                      DCSREAS != "Death"))

part3 <- tabular( Heading("")*DCSREAS*
                    Heading("")*DTHCAUS*DropEmpty(which = "row")*
                    Heading()*countpercentid*Arguments(ARM = TRT01A)*
                    Heading()*USUBJID ~
                  Heading()*TRT01A, 
                  data = subset(adsl, EOSSTT == "Discontinued" &
                                      DCSREAS == "Death"))

useGroupLabels(rbind(heading, part1, part2, part3), 
               indent = "&emsp;")
A: Drug X B: Placebo C: Combination
(N=134) (N=134) (N=132)
Completed 68 (50.75%) 66 (49.25%) 73 (55.30%)
Ongoing 24 (17.91%) 28 (20.90%) 21 (15.91%)
Discontinued      
 Adverse Event 3 (2.24%) 6 (4.48%) 5 (3.79%)
 Lack of Efficacy 2 (1.49%) 2 (1.49%) 3 (2.27%)
 Physician Decision 2 (1.49%) 3 (2.24%) 2 (1.52%)
 Protocol Violation 5 (3.73%) 3 (2.24%) 4 (3.03%)
 Withdrawal by Parent/Guardian 4 (2.99%) 2 (1.49%) 1 (0.76%)
 Withdrawal by Subject 1 (0.75%) 1 (0.75%) 1 (0.76%)
Death      
 Adverse Event 9 (6.72%) 7 (5.22%) 10 (7.58%)
 Disease Progression 8 (5.97%) 6 (4.48%) 6 (4.55%)
 Lost to Follow Up 2 (1.49%) 2 (1.49%) 2 (1.52%)
 Missing 2 (1.49%) 3 (2.24%) 2 (1.52%)
 Post-Study Reporting of Death 1 (0.75%) 2 (1.49%) 1 (0.76%)
 Suicide 2 (1.49%) 2 (1.49%) 1 (0.76%)
 Unknown 1 (0.75%) 1 (0.75%) 0 (0.00%)

4.5.6 tidytlg

Code
resetSession()
library(dplyr)
library(tidytlg)

data("cadsl", package = "random.cdisc.data")

adsl <- cadsl %>% 
  mutate(COMPFL = case_when(EOSSTT == "COMPLETED" ~ "Y",
                            TRUE ~ "N"))

disc <- adsl %>% 
  filter(EOSSTT == "DISCONTINUED")

dth <- adsl %>% 
  filter(DTHFL == "Y")

# Create analysis population counts
tbl1 <- freq(adsl,
             rowvar = "SAFFL",
             colvar = "ARM",
             statlist = statlist("n"),
             rowtext = "Analysis Set: Safety Population",
             subset = SAFFL == "Y")

# Create counts (percentages) for completed patients
tbl2 <- freq(adsl,
             rowvar = "COMPFL",
             colvar = "ARM",
             statlist = statlist("n (x.x%)"),
             rowtext = "Completed",
             subset = COMPFL == "Y")

# Create counts (percentages) for discontinued reasons
tbl3 <- freq(disc,
             denom_df = adsl,
             rowvar = "DCSREAS",
             colvar = "ARM",
             statlist = statlist("n (x.x%)"),
             row_header = "Discontinued")

# Create counts (percentages) for death reasons
tbl4 <- freq(dth,
             denom_df = adsl,
             rowvar = "DTHCAUS",
             colvar = "ARM",
             statlist = statlist("n (x.x%)"),
             row_header = "Death Cause")

# combine analysis results together
tbl <- bind_table(tbl1, tbl2, tbl3, tbl4)

# output the analysis results
gentlg(huxme       = tbl,
       format      = "HTML",
       print.hux = FALSE,
       file        = "Table x.x.x.x",
       orientation = "portrait",
       title = "Study Disposition Summary",
       colheader = c("","A: Drug X","B: Placebo","C: Combination"))
[[1]]
  <div style='border-top :1pt solid; border-bottom :1pt solid; '>              
  <div style = "text-indent: -36px; padding-left: 36px;"> Table                
  x.x.x.x: &emsp; Study Disposition Summary</div>                              
                                       <div           <div           <div      
                                   style='borde   style='borde   style='borde  
                                   r-bottom:1pt   r-bottom:1pt   r-bottom:1pt  
                                    solid'> A:     solid'> B:     solid'> C:   
                                      Drug X        Placebo      Combination   
  <div style='text-indent:             134            134            132       
  -17.76px; padding-left:                                                      
  17.76px'> Analysis Set: Safety                                               
  Population                                                                   
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:          68 (50.7%)     66 (49.3%)     73 (55.3%)   
  -17.76px; padding-left:                                                      
  17.76px'> Completed                                                          
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'> Discontinued                                                       
  <div style='text-indent:           3 (2.2%)       6 (4.5%)       5 (3.8%)    
  -17.76px; padding-left:                                                      
  35.52px'> ADVERSE EVENT                                                      
  <div style='text-indent:          25 (18.7%)     23 (17.2%)     22 (16.7%)   
  -17.76px; padding-left:                                                      
  35.52px'> DEATH                                                              
  <div style='text-indent:           2 (1.5%)       2 (1.5%)       3 (2.3%)    
  -17.76px; padding-left:                                                      
  35.52px'> LACK OF EFFICACY                                                   
  <div style='text-indent:           2 (1.5%)       3 (2.2%)       2 (1.5%)    
  -17.76px; padding-left:                                                      
  35.52px'> PHYSICIAN DECISION                                                 
  <div style='text-indent:           5 (3.7%)       3 (2.2%)       4 (3.0%)    
  -17.76px; padding-left:                                                      
  35.52px'> PROTOCOL VIOLATION                                                 
  <div style='text-indent:           4 (3.0%)       2 (1.5%)       1 (0.8%)    
  -17.76px; padding-left:                                                      
  35.52px'> WITHDRAWAL BY                                                      
  PARENT/GUARDIAN                                                              
  <div style='text-indent:           1 (0.7%)       1 (0.7%)       1 (0.8%)    
  -17.76px; padding-left:                                                      
  35.52px'> WITHDRAWAL BY                                                      
  SUBJECT                                                                      
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'>                                                                    
  <div style='text-indent:                                                     
  -17.76px; padding-left:                                                      
  17.76px'> Death Cause                                                        
  <div style='text-indent:           9 (6.7%)       7 (5.2%)      10 (7.6%)    
  -17.76px; padding-left:                                                      
  35.52px'> ADVERSE EVENT                                                      
  <div style='text-indent:           8 (6.0%)       6 (4.5%)       6 (4.5%)    
  -17.76px; padding-left:                                                      
  35.52px'> DISEASE PROGRESSION                                                
  <div style='text-indent:           2 (1.5%)       2 (1.5%)       2 (1.5%)    
  -17.76px; padding-left:                                                      
  35.52px'> LOST TO FOLLOW UP                                                  
  <div style='text-indent:           2 (1.5%)       3 (2.2%)       2 (1.5%)    
  -17.76px; padding-left:                                                      
  35.52px'> MISSING                                                            
  <div style='text-indent:           1 (0.7%)       2 (1.5%)       1 (0.8%)    
  -17.76px; padding-left:                                                      
  35.52px'> Post-study reporting                                               
  of death                                                                     
  <div style='text-indent:           2 (1.5%)       2 (1.5%)       1 (0.8%)    
  -17.76px; padding-left:                                                      
  35.52px'> SUICIDE                                                            
  <div style='text-indent:           1 (0.7%)       1 (0.7%)          0        
  -17.76px; padding-left:                                                      
  35.52px'> UNKNOWN                                                            
                        <div style='border-bottom:1pt solid'> [table           
  x.x.x.x.html][/home/runner/work/_temp/5a83a2a5-6d01-4ccf-8f57-0c47           
                                          2be60bd2] 28JUN2024, 18:07           

Column names: label, A: Drug X, B: Placebo, C: Combination

4.5.7 tfrmt

Rather than starting with an ADaM, tfrmt assumes users will start with an ARD (Analysis Results Dataset), because of this, making this table will be split into two parts, first to make the ARD and second to format the table.

Code
resetSession()

library(tidyverse)
library(tfrmt)

data("cadsl", package = "random.cdisc.data")


big_n <- cadsl |> 
  dplyr::group_by(ARM) |> 
  dplyr::summarize(
    N = dplyr::n_distinct(USUBJID)
  ) 

disp_summary <- cadsl |>
  dplyr::left_join(big_n, by = "ARM") |> 
  dplyr::group_by(ARM, EOSSTT, DCSREAS, DTHCAUS) |> 
  dplyr::reframe(
     n_subj = n_distinct(USUBJID),
     pct_subj = n_subj/N
  ) |> 
  dplyr::distinct() |> 
  tidyr::pivot_longer(ends_with("subj")) |> 
  dplyr::mutate(
    DCSREAS = if_else(is.na(DCSREAS), EOSSTT, DCSREAS),
    DTHCAUS = if_else(is.na(DTHCAUS), DCSREAS, DTHCAUS),
    EOSSTT = forcats::fct_relevel(EOSSTT, 
                                  "ONGOING", "COMPLETED", "DISCONTINUED")
  ) %>% 
  dplyr::arrange(EOSSTT, DCSREAS, DTHCAUS)

label_N <- big_n |> 
  dplyr::rename(value = N) |> 
  dplyr::mutate(name = "header_n")


disp_ard <- disp_summary |>
  bind_rows(label_N)

## Format Table 
tfrmt(
  column = ARM,
  group = c("EOSSTT", "DCSREAS"),
  param = name,
  value = value,
  label = DTHCAUS
) |>
  # Then we cam combine it with an n percent template 
tfrmt_n_pct(n = "n_subj",
            pct = "pct_subj",
  pct_frmt_when = frmt_when("==1" ~ "", 
                            ">.99" ~ "(>99%)", 
                            "==0" ~ "", 
                            "<.01" ~ "(<1%)", 
                            "TRUE" ~ frmt("(xx.x%)", transform = ~.*100)) 
  ) |>
  #Finally we are going to add some additional formatting
  tfrmt(
    big_n = big_n_structure("header_n"),
    # Aligning on decimal places and spaces
    col_style_plan = col_style_plan(
      col_style_structure(col = matches("[A-Z]:.*"),
                          align = c(".", " "))
    )
  )|> 
  print_to_gt(disp_ard)
A: Drug X N = 134 B: Placebo N = 134 C: Combination N = 132
ONGOING 24 (17.9%) 28 (20.9%) 21 (15.9%)
COMPLETED 68 (50.7%) 66 (49.3%) 73 (55.3%)
DISCONTINUED


  ADVERSE EVENT  3 ( 2.2%)  6 ( 4.5%)  5 ( 3.8%)
  DEATH


    ADVERSE EVENT  9 ( 6.7%)  7 ( 5.2%) 10 ( 7.6%)
    DISEASE PROGRESSION  8 ( 6.0%)  6 ( 4.5%)  6 ( 4.5%)
    LOST TO FOLLOW UP  2 ( 1.5%)  2 ( 1.5%)  2 ( 1.5%)
    MISSING  2 ( 1.5%)  3 ( 2.2%)  2 ( 1.5%)
    Post-study reporting of death  1 (<1%)    2 ( 1.5%)  1 (<1%)  
    SUICIDE  2 ( 1.5%)  2 ( 1.5%)  1 (<1%)  
    UNKNOWN  1 (<1%)    1 (<1%)             
  LACK OF EFFICACY  2 ( 1.5%)  2 ( 1.5%)  3 ( 2.3%)
  PHYSICIAN DECISION  2 ( 1.5%)  3 ( 2.2%)  2 ( 1.5%)
  PROTOCOL VIOLATION  5 ( 3.7%)  3 ( 2.2%)  4 ( 3.0%)
  WITHDRAWAL BY PARENT/GUARDIAN  4 ( 3.0%)  2 ( 1.5%)  1 (<1%)  
  WITHDRAWAL BY SUBJECT  1 (<1%)    1 (<1%)    1 (<1%)