Example Clinical Trials Tables

In rtables: Reporting Tables

suggested_dependent_pkgs <- c("dplyr")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = all(vapply(
    suggested_dependent_pkgs,
    requireNamespace,
    logical(1),
    quietly = TRUE
  ))
)

knitr::opts_chunk$set(comment = "#")

```{css, echo=FALSE} .reveal .r code { white-space: pre; }

## Introduction

In this vignette we create a

* demographic table
* adverse event table
* response table
* time-to-event analysis table

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

Note that all the data is created using random number generators. All
`ex_*` data which is currently attached to the `rtables` package is
provided by the
[`formatters`](https://insightsengineering.github.io/formatters/)
package and was created using the publicly available
[`random.cdisc.data`](https://insightsengineering.github.io/random.cdisc.data/)
R package.

The packages used in this vignette are:

```r
library(rtables)
library(tibble)
library(dplyr)

Demographic Table

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

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

ADSL <- ex_adsl # Example ADSL dataset

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

tbl <- build_table(lyt, ADSL)
tbl

Multiple variables can be analyzed in one analyze() call:

lyt2 <- basic_table() %>%
  split_cols_by("ARM") %>%
  analyze(vars = c("AGE", "BMRKR1"), afun = function(x) {
    in_rows(
      "Mean (sd)" = rcell(c(mean(x), sd(x)), format = "xx.xx (xx.xx)"),
      "Range" = rcell(range(x), format = "xx.xx - xx.xx")
    )
  })

tbl2 <- build_table(lyt2, ADSL)
tbl2

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

s_summary <- function(x) {
  if (is.numeric(x)) {
    in_rows(
      "n" = rcell(sum(!is.na(x)), format = "xx"),
      "Mean (sd)" = rcell(c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE)), format = "xx.xx (xx.xx)"),
      "IQR" = rcell(IQR(x, na.rm = TRUE), format = "xx.xx"),
      "min - max" = rcell(range(x, na.rm = TRUE), format = "xx.xx - xx.xx")
    )
  } else if (is.factor(x)) {
    vs <- as.list(table(x))
    do.call(in_rows, lapply(vs, rcell, format = "xx"))
  } else {
    stop("type not supported")
  }
}

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

s_summary(ADSL$AGE)

and

s_summary(ADSL$SEX)

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

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

summary_tbl <- build_table(summary_lyt, ADSL)
summary_tbl

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

summary_lyt2 <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  analyze("AGE", s_summary) %>%
  analyze("SEX", s_summary)

summary_tbl2 <- build_table(summary_lyt2, ADSL)
summary_tbl2

which leads to the table identical to summary_tbl:

identical(summary_tbl, summary_tbl2)

stopifnot(identical(summary_tbl, summary_tbl2))

In clinical trials analyses the number of patients per column is often referred to as N (rather than the overall population which outside of clinical trials is commonly referred to as N). Column Ns are added by setting the show_colcounts argument in basic_table() to TRUE:

summary_lyt3 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARMCD") %>%
  analyze(c("AGE", "SEX"), s_summary)

summary_tbl3 <- build_table(summary_lyt3, ADSL)
summary_tbl3

Variations on the Demographic Table

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

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

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  analyze(c("AGE", "BMRKR2"), s_summary)

tbl <- build_table(lyt, ADSL)
tbl

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

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX") %>%
  analyze(c("AGE", "BMRKR2"), s_summary)

tbl <- build_table(lyt, ADSL)
tbl

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

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

lyt2 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX") %>%
  analyze(c("AGE", "BMRKR2"), s_summary)

tbl2 <- build_table(lyt2, ADSL_M_F)
tbl2

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

lyt3 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX", split_fun = drop_split_levels, child_labels = "visible") %>%
  analyze(c("AGE", "BMRKR2"), s_summary)

tbl3 <- build_table(lyt3, ADSL_M_F)
tbl3

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

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

lyt4 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "visible") %>%
  analyze(c("AGE", "BMRKR2"), s_summary)

tbl4 <- build_table(lyt4, ADSL_M_F_l)
tbl4

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

lyt5 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "visible") %>%
  analyze("AGE", s_summary, show_labels = "visible") %>%
  analyze("BMRKR2", s_summary, nested = FALSE, show_labels = "visible")

tbl5 <- build_table(lyt5, ADSL_M_F_l)
tbl5

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

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

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

lyt6 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by(
    "SEX",
    labels_var = "lbl_sex",
    split_fun = drop_split_levels,
    child_labels = "visible"
  ) %>%
  analyze("AGE", s_summary) %>%
  analyze("BMRKR2", s_summary, nested = FALSE, show_labels = "visible")

tbl6 <- build_table(lyt6, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl6

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

lyt7 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels) %>%
  summarize_row_groups() %>%
  analyze("AGE", s_summary) %>%
  analyze("BMRKR2", afun = s_summary, nested = FALSE, show_labels = "visible")

tbl7 <- build_table(lyt7, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl7

There are a couple of things to note here:

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

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

lyt8 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels) %>%
  summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) {
    in_rows(
      rcell(nrow(df) * c(1, 1 / .N_col), format = "xx (xx.xx%)"),
      .labels = labelstr
    )
  }) %>%
  analyze("AGE", s_summary) %>%
  analyze("BEP01FL", afun = s_summary, nested = FALSE, show_labels = "visible")

tbl8 <- build_table(lyt8, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl8

Note that cfun, like afun (which is used in analyze()), can operate on either variables, passed via the x argument, or data.frames or tibbles, which are passed via the df argument (afun can optionally request df too). Unlike afun, cfun must accept labelstr as the second argument which gives the default group label (factor level from splitting) and hence it could be modified:

lyt9 <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by("SEX", labels_var = "lbl_sex", split_fun = drop_split_levels, child_labels = "hidden") %>%
  summarize_row_groups(cfun = function(df, labelstr, .N_col, ...) {
    in_rows(
      rcell(nrow(df) * c(1, 1 / .N_col), format = "xx (xx.xx%)"),
      .labels = paste0(labelstr, ": count (perc.)")
    )
  }) %>%
  analyze("AGE", s_summary) %>%
  analyze("BEP01FL", s_summary, nested = FALSE, show_labels = "visible")

tbl9 <- build_table(lyt9, filter(ADSL_NA, SEX %in% c("M", "F")))
tbl9

Using Layouts

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

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

Here is an example that demonstrates the reusability of layouts:

adsl_lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  analyze(c("AGE", "SEX"), afun = s_summary)

adsl_lyt

We can now build a table for ADSL

adsl_tbl <- build_table(adsl_lyt, ADSL)
adsl_tbl

or for all patients that are older than 18:

adsl_f_tbl <- build_table(lyt, ADSL %>% filter(AGE > 18))
adsl_f_tbl

Adverse Events

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

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

set.seed(1)

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

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

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

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

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

ADAE2

Adverse Events By ID

We start by defining an events summary function:

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

So, for a population of 5 patients where

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

we would get the following summary:

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

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

We now use the s_events_patients summary function in a tabulation:

adae_lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  analyze("USUBJID", s_events_patients)

adae_tbl <- build_table(adae_lyt, ADAE2)
adae_tbl

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

rtables handles this by allowing us to override how the column counts are computed. We can specify an alt_counts_df in build_table(). When we do this, rtables calculates the column counts by applying the same column faceting to alt_counts_df as it does to the primary data during tabulation:

adae_adsl_tbl <- build_table(adae_lyt, ADAE2, alt_counts_df = ADSL2)
adae_adsl_tbl

Alternatively, if the desired column counts are already calculated, they can be specified directly via the col_counts argument to build_table(), though specifying an alt_counts_df is the preferred mechanism (the number of rows will be used, but no duplicate checking!!!).

We next calculate this information per system organ class:

adae_soc_lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  analyze("USUBJID", s_events_patients) %>%
  split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE) %>%
  summarize_row_groups("USUBJID", cfun = s_events_patients)

adae_soc_tbl <- build_table(adae_soc_lyt, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl

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

adae_soc_lyt2 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1) %>%
  summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
  analyze("AEDECOD", indent_mod = -1)

adae_soc_tbl2 <- build_table(adae_soc_lyt2, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl2

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

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

table_count_once_per_id <- function(df, termvar = "AEDECOD", idvar = "USUBJID") {
  x <- df[[termvar]]
  id <- df[[idvar]]

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

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

table_count_once_per_id(ADAE2)

So the desired AE table is:

adae_soc_lyt3 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1) %>%
  summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
  analyze("AEDECOD", afun = table_count_once_per_id, show_labels = "hidden", indent_mod = -1)

adae_soc_tbl3 <- build_table(adae_soc_lyt3, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl3

Note that we are missing the overall summary in the first two rows. This can be added with an initial analyze() call.

adae_soc_lyt4 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  analyze("USUBJID", afun = s_events_patients) %>%
  split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1, section_div = "") %>%
  summarize_row_groups("USUBJID", cfun = s_events_patients) %>%
  analyze("AEDECOD", table_count_once_per_id, show_labels = "hidden", indent_mod = -1)

adae_soc_tbl4 <- build_table(adae_soc_lyt4, ADAE2, alt_counts_df = ADSL2)
adae_soc_tbl4

Finally, if we wanted to prune the 0 count rows we can do that with the trim_rows() function:

trim_rows(adae_soc_tbl4)

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

Adverse Events By ID and By Grade

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

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

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

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

  id_sel <- !duplicated(id)

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

table_count_grade_once_per_id(ex_adae, grade_levels = 1:5)

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

adae_grade_lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  analyze(
    "AETOXGR",
    afun = table_count_grade_once_per_id,
    extra_args = list(grade_levels = 1:5),
    var_labels = "- Any adverse events -",
    show_labels = "visible"
  ) %>%
  split_rows_by("AEBODSYS", child_labels = "visible", indent_mod = 1) %>%
  summarize_row_groups(cfun = table_count_grade_once_per_id, format = "xx", indent_mod = 1) %>%
  split_rows_by("AEDECOD", child_labels = "visible", indent_mod = -2) %>%
  analyze(
    "AETOXGR",
    afun = table_count_grade_once_per_id,
    extra_args = list(grade_levels = 1:5),
    show_labels = "hidden"
  )

adae_grade_tbl <- build_table(adae_grade_lyt, ADAE2, alt_counts_df = ADSL2)
adae_grade_tbl

Response Table

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

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

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

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

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

rsp_lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD", ref_group = "ARM A") %>%
  analyze("rsp", s_proportion, show_labels = "hidden")

rsp_tbl <- build_table(rsp_lyt, ADRS_BESRSPI)
rsp_tbl

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

We will now look the implementation of part 2: unstratified analysis comparison vs. control group. Let's start with the analysis function:

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

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

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

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

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

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

rsp_lyt2 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD", ref_group = "ARM A") %>%
  analyze("rsp", s_proportion, show_labels = "hidden") %>%
  analyze(
    "is_rsp", s_unstrat_resp,
    show_labels = "visible",
    var_labels = "Unstratified Response Analysis"
  )

rsp_tbl2 <- build_table(rsp_lyt2, ADRS_BESRSPI)
rsp_tbl2

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

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

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

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

rsp_lyt3 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD", ref_group = "ARM A") %>%
  analyze("rsp", s_proportion, show_labels = "hidden") %>%
  analyze(
    "is_rsp", s_unstrat_resp,
    show_labels = "visible", var_labels = "Unstratified Response Analysis"
  ) %>%
  split_rows_by(
    var = "AVALC",
    split_fun = reorder_split_levels(neworder = c("CR", "PR", "SD", "PD", "NE"), drlevels = TRUE),
    nested = FALSE
  ) %>%
  summarize_row_groups() %>%
  analyze("AVALC", afun = s_prop)

rsp_tbl3 <- build_table(rsp_lyt3, ADRS_BESRSPI)
rsp_tbl3

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

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


rsp_lyt4 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD", ref_group = "ARM A") %>%
  analyze("rsp", s_proportion, show_labels = "hidden") %>%
  analyze(
    "is_rsp", s_unstrat_resp,
    show_labels = "visible", var_labels = "Unstratified Response Analysis"
  ) %>%
  split_rows_by(
    var = "AVALC",
    split_fun = keep_split_levels(c("CR", "PR", "SD", "PD"), reorder = TRUE),
    nested = FALSE
  ) %>%
  summarize_row_groups(cfun = function(df, labelstr, .N_col) {
    in_rows(nrow(df) * c(1, 1 / .N_col), .formats = "xx (xx.xx%)", .labels = rsp_label(labelstr))
  }) %>%
  analyze("AVALC", afun = s_prop) %>%
  analyze("AVALC", afun = function(x, .N_col) {
    in_rows(rcell(sum(x == "NE") * c(1, 1 / .N_col), format = "xx.xx (xx.xx%)"), .labels = rsp_label("NE"))
  }, nested = FALSE)

rsp_tbl4 <- build_table(rsp_lyt4, ADRS_BESRSPI)
rsp_tbl4

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

Time to Event Analysis Table

The time to event analysis table that will be constructed consists of four parts:

1. Overall subject counts
2. Censored subjects summary
3. Cox proportional-hazards analysis
4. Time-to-event analysis

The table is constructed by sequential use of the analyze() function, with four custom analysis functions corresponding to each of the four parts listed above. In addition the table includes referential footnotes relevant to the table contents. The table will be faceted column-wise by arm.

First we will start by loading the necessary packages and preparing the data to be used in the construction of this table.

library(survival)

adtte <- ex_adaette %>%
  dplyr::filter(PARAMCD == "AETTE2", SAFFL == "Y")

# Add censoring to data for example
adtte[adtte$AVAL > 1.0, ] <- adtte[adtte$AVAL > 1.0, ] %>% mutate(AVAL = 1.0, CNSR = 1)

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

The adtte dataset will be used in preparing the models while the adtte2 dataset handles missing values in the "Censor Date Description" column and will be used to produce the final table. We add censoring into the data for example purposes.

Next we create a basic analysis function, a_count_subjs which prints the overall unique subject counts and percentages within the data.

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%)")
  )
}

Then an analysis function is created to generate the counts of censored subjects for each level of a factor variable in the dataset. In this case the cnsr_counter function will be applied with the CNSDTDSC variable which contains a censor date description for each censored subject.

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%)"))
}

This function generates counts and fractions of unique subjects corresponding to each factor level, excluding missing values (uncensored patients).

A Cox proportional-hazards (Cox P-H) analysis is generated next with a third custom analysis function, a_cph. Prior to creating the analysis function, the Cox P-H model is fit to our data using the coxph() and Surv() functions from the survival package. Then this model is used as input to the a_cph analysis function which returns hazard ratios, 95% confidence intervals, and p-values comparing against the reference group - in this case the leftmost column.

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

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])
    sdf <- survdiff(Surv(AVAL, CNSR == 0) ~ ACTARM + 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)")
  )
}

The fourth and final analysis function, a_tte, generates a time to first adverse event table with three rows corresponding to Median, 95% Confidence Interval, and Min Max respectively. First a survival table is constructed from the summary table of a survival model using the survfit() and Surv() functions from the survival package. This table is then given as input to a_tte which produces the table of time to first adverse event consisting of the previously mentioned summary statistics.

surv_tbl <- as.data.frame(
  summary(survfit(Surv(AVAL, CNSR == 0) ~ ACTARM, data = adtte, conf.type = "log-log"))$table
) %>%
  dplyr::mutate(
    ACTARM = factor(gsub("ACTARM=", "", row.names(.)), levels = levels(adtte$ACTARM)),
    ind = FALSE
  )

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()
  if (all(df$CNSR[df$AVAL == minmax[2]])) {
    mm_val_str <- paste0(mm_val_str, "*")
    rowfn <- "* indicates censoring"
  }
  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"),
    .row_footnotes = list(NULL, NULL, rowfn)
  )
}

Additionally, the a_tte function creates a referential footnote within the table to indicate where censoring occurred in the data.

Now we are able to use these four analysis functions to build our time to event analysis table.

lyt <- basic_table(show_colcounts = TRUE) %>%
  ## Column faceting
  split_cols_by("ARM", ref_group = "A: Drug X") %>%
  ## Overall count
  analyze("USUBJID", a_count_subjs, show_labels = "hidden") %>%
  ## Censored subjects summary
  analyze("CNSDTDSC", cnsr_counter, var_labels = "Censored Subjects", show_labels = "visible") %>%
  ## Cox P-H analysis
  analyze("ARM", a_cph, extra_args = list(full_cox_fit = cph), show_labels = "hidden") %>%
  ## Time-to-event analysis
  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)

We set the show_colcounts argument of basic_table() to TRUE to first print the total subject counts for each column. Next we use split_cols_by() to split the table into three columns corresponding to the three different levels of ARM, and specify that the first arm, "A: Drug X" should act as the reference group to be compared against - this reference group is used for the Cox P-H analysis. Then we call analyze() sequentially using each of the four custom analysis functions as argument afun and specifying additional arguments where necessary. Then we use build_table() to construct our rtable using the adtte2 dataset.

Finally, we annotate the table using the fnotes_at_path() function to specify that product-limit estimates are used to calculate the statistics listed under the "Time to first adverse event" heading within the table. The referential footnote created earlier in the time-to-event analysis function (a_tte) is also displayed.

fnotes_at_path(
  tbl_tte,
  c("ma_USUBJID_CNSDTDSC_ARM_kapmeier", "kapmeier")
) <- "Product-limit (Kaplan-Meier) estimates."

tbl_tte

rtables documentation built on June 20, 2025, 1:09 a.m.