Package 'tern'

Description

Package to create tables, listings and graphs to analyze clinical trials data.

Author(s)

Maintainer: Joe Zhu [email protected]

Authors:

• Daniel Sabanés Bové [email protected]

• Jana Stoilova [email protected]

• Davide Garolini [email protected]

• Emily de la Rua [email protected]

• Abinaya Yogasekaram [email protected]

• Heng Wang [email protected]

• Francois Collin

• Adrian Waddell [email protected]

• Pawel Rucki [email protected]

• Chendi Liao [email protected]

• Jennifer Li [email protected]

Other contributors:

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/tern/

• https://github.com/insightsengineering/tern/

• Report bugs at https://github.com/insightsengineering/tern/issues

Description

Wrapper function for rtables::add_combo_levels() which configures settings for the risk difference column to be added to an rtables object. To add a risk difference column to a table, this function should be used as split_fun in calls to rtables::split_cols_by(), followed by setting argument riskdiff to TRUE in all following analyze function calls.

Usage

add_riskdiff(
  arm_x,
  arm_y,
  col_label = paste0("Risk Difference (%) (95% CI)", if (length(arm_y) > 1)
    paste0("\n", arm_x, " vs. ", arm_y)),
  pct = TRUE
)

Arguments

Value

A closure suitable for use as a split function (split_fun) within rtables::split_cols_by() when creating a table layout.

See Also

stat_propdiff_ci() for details on risk difference calculation.

Examples

adae <- tern_ex_adae
adae$AESEV <- factor(adae$AESEV)

lyt <- basic_table() %>%
  split_cols_by("ARMCD", split_fun = add_riskdiff(arm_x = "ARM A", arm_y = c("ARM B", "ARM C"))) %>%
  count_occurrences_by_grade(
    var = "AESEV",
    riskdiff = TRUE
  )

tbl <- build_table(lyt, df = adae)
tbl

Description

This works analogously to rtables::add_colcounts() but on the rows. This function is a wrapper for rtables::summarize_row_groups().

Usage

add_rowcounts(lyt, alt_counts = FALSE)

Arguments

Value

A modified layout where the latest row split labels now have the row-wise total counts (i.e. without column-based subsetting) attached in parentheses.

Note

Row count values are contained in these row count rows but are not displayed so that they are not considered zero rows by default when pruning.

Examples

basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  split_rows_by("RACE", split_fun = drop_split_levels) %>%
  add_rowcounts() %>%
  analyze("AGE", afun = list_wrap_x(summary), format = "xx.xx") %>%
  build_table(DM)

Description

Usage

aesi_label(aesi, scope = NULL)

Arguments

Value

A string with the standard label for the AE basket.

Examples

adae <- tern_ex_adae

# Standardized query label includes scope.
aesi_label(adae$SMQ01NAM, scope = adae$SMQ01SC)

# Customized query label.
aesi_label(adae$CQ01NAM)

Description

These functions are wrappers of rtables::analyze_colvars() which apply corresponding tern statistics functions to add an analysis to a given table layout. In particular, these functions where designed to have the analysis methods split into different columns.

• analyze_vars_in_cols(): fundamental tabulation of analysis methods onto columns. In other words, the analysis methods are defined in the column space, i.e. they become column labels. By changing the variable vector, the list of functions can be applied on different variables, with the caveat of having the same number of statistical functions.

• tabulate_rsp_subgroups(): similarly to analyze_vars_in_cols, this function combines analyze_colvars and summarize_row_groups in a compact way to produce standard tables that show analysis methods as columns.

• tabulate_survival_subgroups(): this function is very similar to the above, but it is used for other tables.

• analyze_patients_exposure_in_cols(): based only on analyze_colvars. It needs summarize_patients_exposure_in_cols() to leverage nesting of label rows analysis with rtables::summarize_row_groups().

• summarize_coxreg(): generally based on rtables::summarize_row_groups(), it behaves similarly to ⁠tabulate_*⁠ functions described above as it is designed to provide specific standard tables that may contain nested structure with a combination of summarize_row_groups() and rtables::analyze_colvars().

See Also

• summarize_functions for functions which are wrappers for rtables::summarize_row_groups().

• analyze_functions for functions which are wrappers for rtables::analyze().

Description

These functions are wrappers of rtables::analyze() which apply corresponding tern statistics functions to add an analysis to a given table layout:

• analyze_num_patients()

• analyze_vars()

• compare_vars()

• count_abnormal()

• count_abnormal_by_baseline()

• count_abnormal_by_marked()

• count_abnormal_by_worst_grade()

• count_cumulative()

• count_missed_doses()

• count_occurrences()

• count_occurrences_by_grade()

• count_patients_events_in_cols()

• count_patients_with_event()

• count_patients_with_flags()

• count_values()

• coxph_pairwise()

• estimate_incidence_rate()

• estimate_multinomial_rsp()

• estimate_odds_ratio()

• estimate_proportion()

• estimate_proportion_diff()

• summarize_ancova()

• summarize_colvars(): even if this function uses rtables::analyze_colvars(), it applies the analysis methods as different rows for one or more variables that are split into different columns. In comparison, analyze_colvars_functions leverage analyze_colvars to have the context split in rows and the analysis methods in columns.

• summarize_change()

• surv_time()

• surv_timepoint()

• test_proportion_diff()

See Also

• analyze_colvars_functions for functions that are wrappers for rtables::analyze_colvars().

• summarize_functions for functions which are wrappers for rtables::summarize_row_groups().

Description

The analyze function analyze_vars() creates a layout element to summarize one or more variables, using the S3 generic function s_summary() to calculate a list of summary statistics. A list of all available statistics for numeric variables can be viewed by running get_stats("analyze_vars_numeric") and for non-numeric variables by running get_stats("analyze_vars_counts"). Use the .stats parameter to specify the statistics to include in your output summary table.

Usage

analyze_vars(
  lyt,
  vars,
  var_labels = vars,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  na.rm = TRUE,
  show_labels = "default",
  table_names = vars,
  section_div = NA_character_,
  .stats = c("n", "mean_sd", "median", "range", "count_fraction"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_summary(x, na.rm = TRUE, denom, .N_row, .N_col, .var, ...)

## S3 method for class 'numeric'
s_summary(
  x,
  na.rm = TRUE,
  denom,
  .N_row,
  .N_col,
  .var,
  control = control_analyze_vars(),
  ...
)

## S3 method for class 'factor'
s_summary(
  x,
  na.rm = TRUE,
  denom = c("n", "N_col", "N_row"),
  .N_row,
  .N_col,
  ...
)

## S3 method for class 'character'
s_summary(
  x,
  na.rm = TRUE,
  denom = c("n", "N_col", "N_row"),
  .N_row,
  .N_col,
  .var,
  verbose = TRUE,
  ...
)

## S3 method for class 'logical'
s_summary(
  x,
  na.rm = TRUE,
  denom = c("n", "N_col", "N_row"),
  .N_row,
  .N_col,
  ...
)

a_summary(
  x,
  .N_col,
  .N_row,
  .var = NULL,
  .df_row = NULL,
  .ref_group = NULL,
  .in_ref_col = FALSE,
  compare = FALSE,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na.rm = TRUE,
  na_str = default_na_str(),
  ...
)

Arguments

Details

Automatic digit formatting: The number of digits to display can be automatically determined from the analyzed variable(s) (vars) for certain statistics by setting the statistic format to "auto" in .formats. This utilizes the format_auto() formatting function. Note that only data for the current row & variable (for all columns) will be considered (.df_row[[.var]], see rtables::additional_fun_params) and not the whole dataset.

Value

• analyze_vars() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_summary() to the table layout.

• s_summary() returns different statistics depending on the class of x.

• If x is of class numeric, returns a list with the following named numeric items:

  • n: The length() of x.

  • sum: The sum() of x.

  • mean: The mean() of x.

  • sd: The stats::sd() of x.

  • se: The standard error of x mean, i.e.: (sd(x) / sqrt(length(x))).

  • mean_sd: The mean() and stats::sd() of x.

  • mean_se: The mean() of x and its standard error (see above).

  • mean_ci: The CI for the mean of x (from stat_mean_ci()).

  • mean_sei: The SE interval for the mean of x, i.e.: (mean() -/+ stats::sd() / sqrt()).

  • mean_sdi: The SD interval for the mean of x, i.e.: (mean() -/+ stats::sd()).

  • mean_pval: The two-sided p-value of the mean of x (from stat_mean_pval()).

  • median: The stats::median() of x.

  • mad: The median absolute deviation of x, i.e.: (stats::median() of xc, where xc = x - stats::median()).

  • median_ci: The CI for the median of x (from stat_median_ci()).

  • quantiles: Two sample quantiles of x (from stats::quantile()).

  • iqr: The stats::IQR() of x.

  • range: The range_noinf() of x.

  • min: The max() of x.

  • max: The min() of x.

  • median_range: The median() and range_noinf() of x.

  • cv: The coefficient of variation of x, i.e.: (stats::sd() / mean() * 100).

  • geom_mean: The geometric mean of x, i.e.: (exp(mean(log(x)))).

  • geom_cv: The geometric coefficient of variation of x, i.e.: (sqrt(exp(sd(log(x)) ^ 2) - 1) * 100).

• If x is of class factor or converted from character, returns a list with named numeric items:

  • n: The length() of x.

  • count: A list with the number of cases for each level of the factor x.

  • count_fraction: Similar to count but also includes the proportion of cases for each level of the factor x relative to the denominator, or NA if the denominator is zero.

• If x is of class logical, returns a list with named numeric items:

  • n: The length() of x (possibly after removing NAs).

  • count: Count of TRUE in x.

  • count_fraction: Count and proportion of TRUE in x relative to the denominator, or NA if the denominator is zero. Note that NAs in x are never counted or leading to NA here.

• a_summary() returns the corresponding list with formatted rtables::CellValue().

Functions

• analyze_vars(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• s_summary(): S3 generic function to produces a variable summary.

• s_summary(numeric): Method for numeric class.

• s_summary(factor): Method for factor class.

• s_summary(character): Method for character class. This makes an automatic conversion to factor (with a warning) and then forwards to the method for factors.

• s_summary(logical): Method for logical class.

• a_summary(): Formatted analysis function which is used as afun in analyze_vars() and compare_vars() and as cfun in summarize_colvars().

Note

• If x is an empty vector, NA is returned. This is the expected feature so as to return rcell content in rtables when the intersection of a column and a row delimits an empty data selection.

• When the mean function is applied to an empty vector, NA will be returned instead of NaN, the latter being standard behavior in R.

• If x is an empty factor, a list is still returned for counts with one element per factor level. If there are no levels in x, the function fails.

• If factor variables contain NA, these NA values are excluded by default. To include NA values set na.rm = FALSE and missing values will be displayed as an NA level. Alternatively, an explicit factor level can be defined for NA values during pre-processing via df_explicit_na() - the default na_level ("<Missing>") will also be excluded when na.rm is set to TRUE.

• Automatic conversion of character to factor does not guarantee that the table can be generated correctly. In particular for sparse tables this very likely can fail. It is therefore better to always pre-process the dataset such that factors are manually created from character variables before passing the dataset to rtables::build_table().

• To use for comparison (with additional p-value statistic), parameter compare must be set to TRUE.

• Ensure that either all NA values are converted to an explicit NA level or all NA values are left as is.

Examples

## Fabricated dataset.
dta_test <- data.frame(
  USUBJID = rep(1:6, each = 3),
  PARAMCD = rep("lab", 6 * 3),
  AVISIT  = rep(paste0("V", 1:3), 6),
  ARM     = rep(LETTERS[1:3], rep(6, 3)),
  AVAL    = c(9:1, rep(NA, 9))
)

# `analyze_vars()` in `rtables` pipelines
## Default output within a `rtables` pipeline.
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by(var = "AVISIT") %>%
  analyze_vars(vars = "AVAL")

build_table(l, df = dta_test)

## Select and format statistics output.
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by(var = "AVISIT") %>%
  analyze_vars(
    vars = "AVAL",
    .stats = c("n", "mean_sd", "quantiles"),
    .formats = c("mean_sd" = "xx.x, xx.x"),
    .labels = c(n = "n", mean_sd = "Mean, SD", quantiles = c("Q1 - Q3"))
  )

build_table(l, df = dta_test)

## Use arguments interpreted by `s_summary`.
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  split_rows_by(var = "AVISIT") %>%
  analyze_vars(vars = "AVAL", na.rm = FALSE)

build_table(l, df = dta_test)

## Handle `NA` levels first when summarizing factors.
dta_test$AVISIT <- NA_character_
dta_test <- df_explicit_na(dta_test)
l <- basic_table() %>%
  split_cols_by(var = "ARM") %>%
  analyze_vars(vars = "AVISIT", na.rm = FALSE)

build_table(l, df = dta_test)

# auto format
dt <- data.frame("VAR" = c(0.001, 0.2, 0.0011000, 3, 4))
basic_table() %>%
  analyze_vars(
    vars = "VAR",
    .stats = c("n", "mean", "mean_sd", "range"),
    .formats = c("mean_sd" = "auto", "range" = "auto")
  ) %>%
  build_table(dt)

# `s_summary.numeric`

## Basic usage: empty numeric returns NA-filled items.
s_summary(numeric())

## Management of NA values.
x <- c(NA_real_, 1)
s_summary(x, na.rm = TRUE)
s_summary(x, na.rm = FALSE)

x <- c(NA_real_, 1, 2)
s_summary(x, stats = NULL)

## Benefits in `rtables` contructions:
dta_test <- data.frame(
  Group = rep(LETTERS[1:3], each = 2),
  sub_group = rep(letters[1:2], each = 3),
  x = 1:6
)

## The summary obtained in with `rtables`:
basic_table() %>%
  split_cols_by(var = "Group") %>%
  split_rows_by(var = "sub_group") %>%
  analyze(vars = "x", afun = s_summary) %>%
  build_table(df = dta_test)

## By comparison with `lapply`:
X <- split(dta_test, f = with(dta_test, interaction(Group, sub_group)))
lapply(X, function(x) s_summary(x$x))

# `s_summary.factor`

## Basic usage:
s_summary(factor(c("a", "a", "b", "c", "a")))

# Empty factor returns zero-filled items.
s_summary(factor(levels = c("a", "b", "c")))

## Management of NA values.
x <- factor(c(NA, "Female"))
x <- explicit_na(x)
s_summary(x, na.rm = TRUE)
s_summary(x, na.rm = FALSE)

## Different denominators.
x <- factor(c("a", "a", "b", "c", "a"))
s_summary(x, denom = "N_row", .N_row = 10L)
s_summary(x, denom = "N_col", .N_col = 20L)

# `s_summary.character`

## Basic usage:
s_summary(c("a", "a", "b", "c", "a"), .var = "x", verbose = FALSE)
s_summary(c("a", "a", "b", "c", "a", ""), .var = "x", na.rm = FALSE, verbose = FALSE)

# `s_summary.logical`

## Basic usage:
s_summary(c(TRUE, FALSE, TRUE, TRUE))

# Empty factor returns zero-filled items.
s_summary(as.logical(c()))

## Management of NA values.
x <- c(NA, TRUE, FALSE)
s_summary(x, na.rm = TRUE)
s_summary(x, na.rm = FALSE)

## Different denominators.
x <- c(TRUE, FALSE, TRUE, TRUE)
s_summary(x, denom = "N_row", .N_row = 10L)
s_summary(x, denom = "N_col", .N_col = 20L)

a_summary(factor(c("a", "a", "b", "c", "a")), .N_row = 10, .N_col = 10)
a_summary(
  factor(c("a", "a", "b", "c", "a")),
  .ref_group = factor(c("a", "a", "b", "c")), compare = TRUE
)

a_summary(c("A", "B", "A", "C"), .var = "x", .N_col = 10, .N_row = 10, verbose = FALSE)
a_summary(
  c("A", "B", "A", "C"),
  .ref_group = c("B", "A", "C"), .var = "x", compare = TRUE, verbose = FALSE
)

a_summary(c(TRUE, FALSE, FALSE, TRUE, TRUE), .N_row = 10, .N_col = 10)
a_summary(
  c(TRUE, FALSE, FALSE, TRUE, TRUE),
  .ref_group = c(TRUE, FALSE), .in_ref_col = TRUE, compare = TRUE
)

a_summary(rnorm(10), .N_col = 10, .N_row = 20, .var = "bla")
a_summary(rnorm(10, 5, 1), .ref_group = rnorm(20, -5, 1), .var = "bla", compare = TRUE)

Description

The layout-creating function analyze_vars_in_cols() creates a layout element to generate a column-wise analysis table.

This function sets the analysis methods as column labels and is a wrapper for rtables::analyze_colvars(). It was designed principally for PK tables.

Usage

analyze_vars_in_cols(
  lyt,
  vars,
  ...,
  .stats = c("n", "mean", "sd", "se", "cv", "geom_cv"),
  .labels = c(n = "n", mean = "Mean", sd = "SD", se = "SE", cv = "CV (%)", geom_cv =
    "CV % Geometric Mean"),
  row_labels = NULL,
  do_summarize_row_groups = FALSE,
  split_col_vars = TRUE,
  imp_rule = NULL,
  avalcat_var = "AVALCAT1",
  cache = FALSE,
  .indent_mods = NULL,
  na_str = default_na_str(),
  nested = TRUE,
  .formats = NULL,
  .aligns = NULL
)

Arguments

Value

A layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will summarize the given variables, arrange the output in columns, and add it to the table layout.

Note

• This is an experimental implementation of rtables::summarize_row_groups() and rtables::analyze_colvars() that may be subjected to changes as rtables extends its support to more complex analysis pipelines in the column space. We encourage users to read the examples carefully and file issues for different use cases.

• In this function, labelstr behaves atypically. If labelstr = NULL (the default), row labels are assigned automatically as the split values if do_summarize_row_groups = FALSE (the default), and as the group label if do_summarize_row_groups = TRUE.

See Also

analyze_vars(), rtables::analyze_colvars().

Examples

library(dplyr)

# Data preparation
adpp <- tern_ex_adpp %>% h_pkparam_sort()

lyt <- basic_table() %>%
  split_rows_by(var = "STRATA1", label_pos = "topleft") %>%
  split_rows_by(
    var = "SEX",
    label_pos = "topleft",
    child_labels = "hidden"
  ) %>% # Removes duplicated labels
  analyze_vars_in_cols(vars = "AGE")
result <- build_table(lyt = lyt, df = adpp)
result

# By selecting just some statistics and ad-hoc labels
lyt <- basic_table() %>%
  split_rows_by(var = "ARM", label_pos = "topleft") %>%
  split_rows_by(
    var = "SEX",
    label_pos = "topleft",
    child_labels = "hidden",
    split_fun = drop_split_levels
  ) %>%
  analyze_vars_in_cols(
    vars = "AGE",
    .stats = c("n", "cv", "geom_mean"),
    .labels = c(
      n = "aN",
      cv = "aCV",
      geom_mean = "aGeomMean"
    )
  )
result <- build_table(lyt = lyt, df = adpp)
result

# Changing row labels
lyt <- basic_table() %>%
  analyze_vars_in_cols(
    vars = "AGE",
    row_labels = "some custom label"
  )
result <- build_table(lyt, df = adpp)
result

# Pharmacokinetic parameters
lyt <- basic_table() %>%
  split_rows_by(
    var = "TLG_DISPLAY",
    split_label = "PK Parameter",
    label_pos = "topleft",
    child_labels = "hidden"
  ) %>%
  analyze_vars_in_cols(
    vars = "AVAL"
  )
result <- build_table(lyt, df = adpp)
result

# Multiple calls (summarize label and analyze underneath)
lyt <- basic_table() %>%
  split_rows_by(
    var = "TLG_DISPLAY",
    split_label = "PK Parameter",
    label_pos = "topleft"
  ) %>%
  analyze_vars_in_cols(
    vars = "AVAL",
    do_summarize_row_groups = TRUE # does a summarize level
  ) %>%
  split_rows_by("SEX",
    child_labels = "hidden",
    label_pos = "topleft"
  ) %>%
  analyze_vars_in_cols(
    vars = "AVAL",
    split_col_vars = FALSE # avoids re-splitting the columns
  )
result <- build_table(lyt, df = adpp)
result

Description

Helper layout-creating function to append the variable labels of a given variables vector from a given dataset in the top left corner. If a variable label is not found then the variable name itself is used instead. Multiple variable labels are concatenated with slashes.

Usage

append_varlabels(lyt, df, vars, indent = 0L)

Arguments

Value

A modified layout with the new variable label(s) added to the top-left material.

Note

This is not an optimal implementation of course, since we are using here the data set itself during the layout creation. When we have a more mature rtables implementation then this will also be improved or not necessary anymore.

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  split_rows_by("SEX") %>%
  append_varlabels(DM, "SEX") %>%
  analyze("AGE", afun = mean) %>%
  append_varlabels(DM, "AGE", indent = 1)
build_table(lyt, DM)

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by("SEX") %>%
  analyze("AGE", afun = mean) %>%
  append_varlabels(DM, c("SEX", "AGE"))
build_table(lyt, DM)

Description

Arrange grobs as a new grob with n * m (rows * cols) layout.

Usage

arrange_grobs(
  ...,
  grobs = list(...),
  ncol = NULL,
  nrow = NULL,
  padding_ht = grid::unit(2, "line"),
  padding_wt = grid::unit(2, "line"),
  vp = NULL,
  gp = NULL,
  name = NULL
)

Arguments

Value

A grob.

Examples

library(grid)


num <- lapply(1:9, textGrob)
grid::grid.newpage()
grid.draw(arrange_grobs(grobs = num, ncol = 2))

showViewport()

g1 <- circleGrob(gp = gpar(col = "blue"))
g2 <- circleGrob(gp = gpar(col = "red"))
g3 <- textGrob("TEST TEXT")
grid::grid.newpage()
grid.draw(arrange_grobs(g1, g2, g3, nrow = 2))

showViewport()

grid::grid.newpage()
grid.draw(arrange_grobs(g1, g2, g3, ncol = 3))

grid::grid.newpage()
grid::pushViewport(grid::viewport(layout = grid::grid.layout(1, 2)))
vp1 <- grid::viewport(layout.pos.row = 1, layout.pos.col = 2)
grid.draw(arrange_grobs(g1, g2, g3, ncol = 2, vp = vp1))

showViewport()

Description

This is a new generic function to convert objects to rtable tables.

Usage

as.rtable(x, ...)

## S3 method for class 'data.frame'
as.rtable(x, format = "xx.xx", ...)

Arguments

Value

An rtables table object. Note that the concrete class will depend on the method used.

Methods (by class)

• as.rtable(data.frame): Method for converting a data.frame that contains numeric columns to rtable.

Examples

x <- data.frame(
  a = 1:10,
  b = rnorm(10)
)
as.rtable(x)

Description

CombinationFunction is an S4 class which extends standard functions. These are special functions that can be combined and negated with the logical operators.

Usage

## S4 method for signature 'CombinationFunction,CombinationFunction'
e1 & e2

## S4 method for signature 'CombinationFunction,CombinationFunction'
e1 | e2

## S4 method for signature 'CombinationFunction'
!x

Arguments

Value

A logical value indicating whether the left hand side of the equation equals the right hand side.

Functions

• e1 & e2: Logical "AND" combination of CombinationFunction functions. The resulting object is of the same class, and evaluates the two argument functions. The result is then the "AND" of the two individual results.

• e1 | e2: Logical "OR" combination of CombinationFunction functions. The resulting object is of the same class, and evaluates the two argument functions. The result is then the "OR" of the two individual results.

• `!`(CombinationFunction): Logical negation of CombinationFunction functions. The resulting object is of the same class, and evaluates the original function. The result is then the opposite of this results.

Examples

higher <- function(a) {
  force(a)
  CombinationFunction(
    function(x) {
      x > a
    }
  )
}

lower <- function(b) {
  force(b)
  CombinationFunction(
    function(x) {
      x < b
    }
  )
}

c1 <- higher(5)
c2 <- lower(10)
c3 <- higher(5) & lower(10)
c3(7)

Description

Simplifies the estimation of column counts, especially when group combination is required.

Usage

combine_counts(fct, groups_list = NULL)

Arguments

Value

A vector of column counts.

See Also

combine_groups()

Examples

ref <- c("A: Drug X", "B: Placebo")
groups <- combine_groups(fct = DM$ARM, ref = ref)

col_counts <- combine_counts(
  fct = DM$ARM,
  groups_list = groups
)

basic_table() %>%
  split_cols_by_groups("ARM", groups) %>%
  add_colcounts() %>%
  analyze_vars("AGE") %>%
  build_table(DM, col_counts = col_counts)

ref <- "A: Drug X"
groups <- combine_groups(fct = DM$ARM, ref = ref)
col_counts <- combine_counts(
  fct = DM$ARM,
  groups_list = groups
)

basic_table() %>%
  split_cols_by_groups("ARM", groups) %>%
  add_colcounts() %>%
  analyze_vars("AGE") %>%
  build_table(DM, col_counts = col_counts)

Description

Facilitate the re-combination of groups divided as reference and treatment groups; it helps in arranging groups of columns in the rtables framework and teal modules.

Usage

combine_groups(fct, ref = NULL, collapse = "/")

Arguments

Value

A list with first item ref (reference) and second item trt (treatment).

Examples

groups <- combine_groups(
  fct = DM$ARM,
  ref = c("B: Placebo")
)

basic_table() %>%
  split_cols_by_groups("ARM", groups) %>%
  add_colcounts() %>%
  analyze_vars("AGE") %>%
  build_table(DM)

Description

Combine specified old factor Levels in a single new level.

Usage

combine_levels(x, levels, new_level = paste(levels, collapse = "/"))

Arguments

Value

A factor with the new levels.

Examples

x <- factor(letters[1:5], levels = letters[5:1])
combine_levels(x, levels = c("a", "b"))

combine_levels(x, c("e", "b"))

Description

Element-wise combination of two vectors

Usage

combine_vectors(x, y)

Arguments

Value

A list where each element combines corresponding elements of x and y.

Examples

combine_vectors(1:3, 4:6)

Description

The analyze function compare_vars() creates a layout element to summarize and compare one or more variables, using the S3 generic function s_summary() to calculate a list of summary statistics. A list of all available statistics for numeric variables can be viewed by running get_stats("analyze_vars_numeric", add_pval = TRUE) and for non-numeric variables by running get_stats("analyze_vars_counts", add_pval = TRUE). Use the .stats parameter to specify the statistics to include in your output summary table.

Prior to using this function in your table layout you must use rtables::split_cols_by() to create a column split on the variable to be used in comparisons, and specify a reference group via the ref_group parameter. Comparisons can be performed for each group (column) against the specified reference group by including the p-value statistic.

Usage

compare_vars(
  lyt,
  vars,
  var_labels = vars,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  na.rm = TRUE,
  show_labels = "default",
  table_names = vars,
  section_div = NA_character_,
  .stats = c("n", "mean_sd", "count_fraction", "pval"),
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

s_compare(x, .ref_group, .in_ref_col, ...)

## S3 method for class 'numeric'
s_compare(x, .ref_group, .in_ref_col, ...)

## S3 method for class 'factor'
s_compare(x, .ref_group, .in_ref_col, denom = "n", na.rm = TRUE, ...)

## S3 method for class 'character'
s_compare(
  x,
  .ref_group,
  .in_ref_col,
  denom = "n",
  na.rm = TRUE,
  .var,
  verbose = TRUE,
  ...
)

## S3 method for class 'logical'
s_compare(x, .ref_group, .in_ref_col, na.rm = TRUE, denom = "n", ...)

Arguments

Value

• compare_vars() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_compare() to the table layout.

• s_compare() returns output of s_summary() and comparisons versus the reference group in the form of p-values.

Functions

• compare_vars(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• s_compare(): S3 generic function to produce a comparison summary.

• s_compare(numeric): Method for numeric class. This uses the standard t-test to calculate the p-value.

• s_compare(factor): Method for factor class. This uses the chi-squared test to calculate the p-value.

• s_compare(character): Method for character class. This makes an automatic conversion to factor (with a warning) and then forwards to the method for factors.

• s_compare(logical): Method for logical class. A chi-squared test is used. If missing values are not removed, then they are counted as FALSE.

Note

• For factor variables, denom for factor proportions can only be n since the purpose is to compare proportions between columns, therefore a row-based proportion would not make sense. Proportion based on N_col would be difficult since we use counts for the chi-squared test statistic, therefore missing values should be accounted for as explicit factor levels.

• If factor variables contain NA, these NA values are excluded by default. To include NA values set na.rm = FALSE and missing values will be displayed as an NA level. Alternatively, an explicit factor level can be defined for NA values during pre-processing via df_explicit_na() - the default na_level ("<Missing>") will also be excluded when na.rm is set to TRUE.

• For character variables, automatic conversion to factor does not guarantee that the table will be generated correctly. In particular for sparse tables this very likely can fail. Therefore it is always better to manually convert character variables to factors during pre-processing.

• For compare_vars(), the column split must define a reference group via ref_group so that the comparison is well defined.

See Also

s_summary() which is used internally to compute a summary within s_compare(), and a_summary() which is used (with compare = TRUE) as the analysis function for compare_vars().

Examples

# `compare_vars()` in `rtables` pipelines

## Default output within a `rtables` pipeline.
lyt <- basic_table() %>%
  split_cols_by("ARMCD", ref_group = "ARM B") %>%
  compare_vars(c("AGE", "SEX"))
build_table(lyt, tern_ex_adsl)

## Select and format statistics output.
lyt <- basic_table() %>%
  split_cols_by("ARMCD", ref_group = "ARM C") %>%
  compare_vars(
    vars = "AGE",
    .stats = c("mean_sd", "pval"),
    .formats = c(mean_sd = "xx.x, xx.x"),
    .labels = c(mean_sd = "Mean, SD")
  )
build_table(lyt, df = tern_ex_adsl)

# `s_compare.numeric`

## Usual case where both this and the reference group vector have more than 1 value.
s_compare(rnorm(10, 5, 1), .ref_group = rnorm(5, -5, 1), .in_ref_col = FALSE)

## If one group has not more than 1 value, then p-value is not calculated.
s_compare(rnorm(10, 5, 1), .ref_group = 1, .in_ref_col = FALSE)

## Empty numeric does not fail, it returns NA-filled items and no p-value.
s_compare(numeric(), .ref_group = numeric(), .in_ref_col = FALSE)

# `s_compare.factor`

## Basic usage:
x <- factor(c("a", "a", "b", "c", "a"))
y <- factor(c("a", "b", "c"))
s_compare(x = x, .ref_group = y, .in_ref_col = FALSE)

## Management of NA values.
x <- explicit_na(factor(c("a", "a", "b", "c", "a", NA, NA)))
y <- explicit_na(factor(c("a", "b", "c", NA)))
s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE)
s_compare(x = x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE)

# `s_compare.character`

## Basic usage:
x <- c("a", "a", "b", "c", "a")
y <- c("a", "b", "c")
s_compare(x, .ref_group = y, .in_ref_col = FALSE, .var = "x", verbose = FALSE)

## Note that missing values handling can make a large difference:
x <- c("a", "a", "b", "c", "a", NA)
y <- c("a", "b", "c", rep(NA, 20))
s_compare(x,
  .ref_group = y, .in_ref_col = FALSE,
  .var = "x", verbose = FALSE
)
s_compare(x,
  .ref_group = y, .in_ref_col = FALSE, .var = "x",
  na.rm = FALSE, verbose = FALSE
)

# `s_compare.logical`

## Basic usage:
x <- c(TRUE, FALSE, TRUE, TRUE)
y <- c(FALSE, FALSE, TRUE)
s_compare(x, .ref_group = y, .in_ref_col = FALSE)

## Management of NA values.
x <- c(NA, TRUE, FALSE)
y <- c(NA, NA, NA, NA, FALSE)
s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = TRUE)
s_compare(x, .ref_group = y, .in_ref_col = FALSE, na.rm = FALSE)

Description

Sets a list of parameters for summaries of descriptive statistics. Typically used internally to specify details for s_summary(). This function family is mainly used by analyze_vars().

Usage

control_analyze_vars(
  conf_level = 0.95,
  quantiles = c(0.25, 0.75),
  quantile_type = 2,
  test_mean = 0
)

Arguments

Value

A list of components with the same names as the arguments.

Description

Auxiliary functions for controlling arguments for formatting the annotation tables that can be added to plots generated via g_km().

Usage

control_surv_med_annot(x = 0.8, y = 0.85, w = 0.32, h = 0.16, fill = TRUE)

control_coxph_annot(
  x = 0.29,
  y = 0.51,
  w = 0.4,
  h = 0.125,
  fill = TRUE,
  ref_lbls = FALSE
)

Arguments

Value

A list of components with the same names as the arguments.

Functions

• control_surv_med_annot(): Control function for formatting the median survival time annotation table. This annotation table can be added in g_km() by setting annot_surv_med=TRUE, and can be configured using the control_surv_med_annot() function by setting it as the control_annot_surv_med argument.

• control_coxph_annot(): Control function for formatting the Cox-PH annotation table. This annotation table can be added in g_km() by setting annot_coxph=TRUE, and can be configured using the control_coxph_annot() function by setting it as the control_annot_coxph argument.

See Also

g_km()

Examples

control_surv_med_annot()

control_coxph_annot()

Description

This is an auxiliary function for controlling arguments for Cox-PH model, typically used internally to specify details of Cox-PH model for s_coxph_pairwise(). conf_level refers to Hazard Ratio estimation.

Usage

control_coxph(
  pval_method = c("log-rank", "wald", "likelihood"),
  ties = c("efron", "breslow", "exact"),
  conf_level = 0.95
)

Arguments

Value

A list of components with the same names as the arguments.

Description

Sets a list of parameters for Cox regression fit. Used internally.

Usage

control_coxreg(
  pval_method = c("wald", "likelihood"),
  ties = c("exact", "efron", "breslow"),
  conf_level = 0.95,
  interaction = FALSE
)

Arguments

Value

A list of items with names corresponding to the arguments.

See Also

fit_coxreg_univar() and fit_coxreg_multivar().

Examples

control_coxreg()

Description

This is an auxiliary function for controlling arguments for the incidence rate, used internally to specify details in s_incidence_rate().

Usage

control_incidence_rate(
  conf_level = 0.95,
  conf_type = c("normal", "normal_log", "exact", "byar"),
  input_time_unit = c("year", "day", "week", "month"),
  num_pt_year = 100
)

Arguments

Value

A list of components with the same names as the arguments.

See Also

incidence_rate

Examples

control_incidence_rate(0.9, "exact", "month", 100)

Description

Default values for variables parameter in g_lineplot function. A variable's default value can be overwritten for any variable.

Usage

control_lineplot_vars(
  x = "AVISIT",
  y = "AVAL",
  group_var = "ARM",
  facet_var = NA,
  paramcd = "PARAMCD",
  y_unit = "AVALU",
  subject_var = "USUBJID"
)

Arguments

Value

A named character vector of variable names.

Examples

control_lineplot_vars()
control_lineplot_vars(group_var = NA)

Description

This is an auxiliary function for controlling arguments for logistic regression models. conf_level refers to the confidence level used for the Odds Ratio CIs.

Usage

control_logistic(response_definition = "response", conf_level = 0.95)

Arguments

Value

A list of components with the same names as the arguments.

Examples

# Standard options.
control_logistic()

# Modify confidence level.
control_logistic(conf_level = 0.9)

# Use a different response definition.
control_logistic(response_definition = "I(response %in% c('CR', 'PR'))")

Description

Sets a list of parameters to use when generating a risk (proportion) difference column. Used as input to the riskdiff parameter of tabulate_rsp_subgroups() and tabulate_survival_subgroups().

Usage

control_riskdiff(
  arm_x = NULL,
  arm_y = NULL,
  format = "xx.x (xx.x - xx.x)",
  col_label = "Risk Difference (%) (95% CI)",
  pct = TRUE
)

Arguments

Value

A list of items with names corresponding to the arguments.

See Also

add_riskdiff(), tabulate_rsp_subgroups(), and tabulate_survival_subgroups().

Examples

control_riskdiff()
control_riskdiff(arm_x = "ARM A", arm_y = "ARM B")

Description

This is an auxiliary function for controlling arguments for STEP calculations.

Usage

control_step(
  biomarker = NULL,
  use_percentile = TRUE,
  bandwidth,
  degree = 0L,
  num_points = 39L
)

Arguments

Value

A list of components with the same names as the arguments, except biomarker which is just used to calculate the bandwidth in case that actual biomarker windows are requested.

Examples

# Provide biomarker values and request actual values to be used,
# so that bandwidth is chosen from range.
control_step(biomarker = 1:10, use_percentile = FALSE)

# Use a global model with quadratic biomarker interaction term.
control_step(bandwidth = NULL, degree = 2)

# Reduce number of points to be used.
control_step(num_points = 10)

Description

This is an auxiliary function for controlling arguments for survfit model, typically used internally to specify details of survfit model for s_surv_time(). conf_level refers to survival time estimation.

Usage

control_surv_time(
  conf_level = 0.95,
  conf_type = c("plain", "log", "log-log"),
  quantiles = c(0.25, 0.75)
)

Arguments

Value

A list of components with the same names as the arguments.

Description

This is an auxiliary function for controlling arguments for survfit model, typically used internally to specify details of survfit model for s_surv_timepoint(). conf_level refers to patient risk estimation at a time point.

Usage

control_surv_timepoint(
  conf_level = 0.95,
  conf_type = c("plain", "log", "log-log")
)

Arguments

Value

A list of components with the same names as the arguments.

Description

The analyze function count_occurrences() creates a layout element to calculate occurrence counts for patients.

This function analyzes the variable(s) supplied to vars and returns a table of occurrence counts for each unique value (or level) of the variable(s). This variable (or variables) must be non-numeric. The id variable is used to indicate unique subject identifiers (defaults to USUBJID).

If there are multiple occurrences of the same value recorded for a patient, the value is only counted once.

The summarize function summarize_occurrences() performs the same function as count_occurrences() except it creates content rows, not data rows, to summarize the current table row/column context and operates on the level of the latest row split or the root of the table if no row splits have occurred.

Usage

count_occurrences(
  lyt,
  vars,
  id = "USUBJID",
  drop = TRUE,
  var_labels = vars,
  show_labels = "hidden",
  riskdiff = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = "count_fraction_fixed_dp",
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

summarize_occurrences(
  lyt,
  var,
  id = "USUBJID",
  drop = TRUE,
  riskdiff = FALSE,
  na_str = default_na_str(),
  ...,
  .stats = "count_fraction_fixed_dp",
  .formats = NULL,
  .indent_mods = NULL,
  .labels = NULL
)

s_count_occurrences(
  df,
  denom = c("N_col", "n", "N_row"),
  .N_col,
  .N_row,
  .df_row,
  drop = TRUE,
  .var = "MHDECOD",
  id = "USUBJID"
)

a_count_occurrences(
  df,
  labelstr = "",
  id = "USUBJID",
  denom = c("N_col", "n", "N_row"),
  drop = TRUE,
  .N_col,
  .N_row,
  .var = NULL,
  .df_row = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na_str = default_na_str()
)

Arguments

Value

• count_occurrences() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences() to the table layout.

• summarize_occurrences() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences() to the table layout.

• s_count_occurrences() returns a list with:

  • count: list of counts with one element per occurrence.

  • count_fraction: list of counts and fractions with one element per occurrence.

  • fraction: list of numerators and denominators with one element per occurrence.

• a_count_occurrences() returns the corresponding list with formatted rtables::CellValue().

Functions

• count_occurrences(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• summarize_occurrences(): Layout-creating function which can take content function arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().

• s_count_occurrences(): Statistics function which counts number of patients that report an occurrence.

• a_count_occurrences(): Formatted analysis function which is used as afun in count_occurrences().

Note

By default, occurrences which don't appear in a given row split are dropped from the table and the occurrences in the table are sorted alphabetically per row split. Therefore, the corresponding layout needs to use split_fun = drop_split_levels in the split_rows_by calls. Use drop = FALSE if you would like to show all occurrences.

Examples

library(dplyr)
df <- data.frame(
  USUBJID = as.character(c(
    1, 1, 2, 4, 4, 4,
    6, 6, 6, 7, 7, 8
  )),
  MHDECOD = c(
    "MH1", "MH2", "MH1", "MH1", "MH1", "MH3",
    "MH2", "MH2", "MH3", "MH1", "MH2", "MH4"
  ),
  ARM = rep(c("A", "B"), each = 6),
  SEX = c("F", "F", "M", "M", "M", "M", "F", "F", "F", "M", "M", "F")
)
df_adsl <- df %>%
  select(USUBJID, ARM) %>%
  unique()

# Create table layout
lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_occurrences(vars = "MHDECOD", .stats = c("count_fraction"))

# Apply table layout to data and produce `rtable` object
tbl <- lyt %>%
  build_table(df, alt_counts_df = df_adsl) %>%
  prune_table()

tbl

# Layout creating function with custom format.
basic_table() %>%
  add_colcounts() %>%
  split_rows_by("SEX", child_labels = "visible") %>%
  summarize_occurrences(
    var = "MHDECOD",
    .formats = c("count_fraction" = "xx.xx (xx.xx%)")
  ) %>%
  build_table(df, alt_counts_df = df_adsl)

# Count unique occurrences per subject.
s_count_occurrences(
  df,
  .N_col = 4L,
  .N_row = 4L,
  .df_row = df,
  .var = "MHDECOD",
  id = "USUBJID"
)

a_count_occurrences(
  df,
  .N_col = 4L,
  .df_row = df,
  .var = "MHDECOD",
  id = "USUBJID"
)

Description

The analyze function count_occurrences_by_grade() creates a layout element to calculate occurrence counts by grade.

This function analyzes primary analysis variable var which indicates toxicity grades. The id variable is used to indicate unique subject identifiers (defaults to USUBJID). The user can also supply a list of custom groups of grades to analyze via the grade_groups parameter. The remove_single argument will remove single grades from the analysis so that only grade groups are analyzed.

If there are multiple grades recorded for one patient only the highest grade level is counted.

The summarize function summarize_occurrences_by_grade() performs the same function as count_occurrences_by_grade() except it creates content rows, not data rows, to summarize the current table row/column context and operates on the level of the latest row split or the root of the table if no row splits have occurred.

Usage

count_occurrences_by_grade(
  lyt,
  var,
  id = "USUBJID",
  grade_groups = list(),
  remove_single = TRUE,
  only_grade_groups = FALSE,
  var_labels = var,
  show_labels = "default",
  riskdiff = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = var,
  .stats = "count_fraction",
  .formats = list(count_fraction = format_count_fraction_fixed_dp),
  .indent_mods = NULL,
  .labels = NULL
)

summarize_occurrences_by_grade(
  lyt,
  var,
  id = "USUBJID",
  grade_groups = list(),
  remove_single = TRUE,
  only_grade_groups = FALSE,
  riskdiff = FALSE,
  na_str = default_na_str(),
  ...,
  .stats = "count_fraction",
  .formats = list(count_fraction = format_count_fraction_fixed_dp),
  .indent_mods = NULL,
  .labels = NULL
)

s_count_occurrences_by_grade(
  df,
  .var,
  .N_row,
  .N_col,
  id = "USUBJID",
  grade_groups = list(),
  remove_single = TRUE,
  only_grade_groups = FALSE,
  denom = c("N_col", "n", "N_row"),
  labelstr = ""
)

a_count_occurrences_by_grade(
  df,
  labelstr = "",
  id = "USUBJID",
  grade_groups = list(),
  remove_single = TRUE,
  only_grade_groups = FALSE,
  denom = c("N_col", "n", "N_row"),
  .N_col,
  .N_row,
  .df_row,
  .var = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na_str = default_na_str()
)

Arguments

Value

• count_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_occurrences_by_grade() to the table layout.

• summarize_occurrences_by_grade() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted content rows containing the statistics from s_count_occurrences_by_grade() to the table layout.

• s_count_occurrences_by_grade() returns a list of counts and fractions with one element per grade level or grade level grouping.

• a_count_occurrences_by_grade() returns the corresponding list with formatted rtables::CellValue().

Functions

• count_occurrences_by_grade(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• summarize_occurrences_by_grade(): Layout-creating function which can take content function arguments and additional format arguments. This function is a wrapper for rtables::summarize_row_groups().

• s_count_occurrences_by_grade(): Statistics function which counts the number of patients by highest grade.

• a_count_occurrences_by_grade(): Formatted analysis function which is used as afun in count_occurrences_by_grade().

See Also

Relevant helper function h_append_grade_groups().

Examples

library(dplyr)

df <- data.frame(
  USUBJID = as.character(c(1:6, 1)),
  ARM = factor(c("A", "A", "A", "B", "B", "B", "A"), levels = c("A", "B")),
  AETOXGR = factor(c(1, 2, 3, 4, 1, 2, 3), levels = c(1:5)),
  AESEV = factor(
    x = c("MILD", "MODERATE", "SEVERE", "MILD", "MILD", "MODERATE", "SEVERE"),
    levels = c("MILD", "MODERATE", "SEVERE")
  ),
  stringsAsFactors = FALSE
)

df_adsl <- df %>%
  select(USUBJID, ARM) %>%
  unique()

# Layout creating function with custom format.
basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_occurrences_by_grade(
    var = "AESEV",
    .formats = c("count_fraction" = "xx.xx (xx.xx%)")
  ) %>%
  build_table(df, alt_counts_df = df_adsl)

# Define additional grade groupings.
grade_groups <- list(
  "-Any-" = c("1", "2", "3", "4", "5"),
  "Grade 1-2" = c("1", "2"),
  "Grade 3-5" = c("3", "4", "5")
)

basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_occurrences_by_grade(
    var = "AETOXGR",
    grade_groups = grade_groups,
    only_grade_groups = TRUE
  ) %>%
  build_table(df, alt_counts_df = df_adsl)

# Layout creating function with custom format.
basic_table() %>%
  add_colcounts() %>%
  split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
  summarize_occurrences_by_grade(
    var = "AESEV",
    .formats = c("count_fraction" = "xx.xx (xx.xx%)")
  ) %>%
  build_table(df, alt_counts_df = df_adsl)

basic_table() %>%
  add_colcounts() %>%
  split_rows_by("ARM", child_labels = "visible", nested = TRUE) %>%
  summarize_occurrences_by_grade(
    var = "AETOXGR",
    grade_groups = grade_groups
  ) %>%
  build_table(df, alt_counts_df = df_adsl)

s_count_occurrences_by_grade(
  df,
  .N_col = 10L,
  .var = "AETOXGR",
  id = "USUBJID",
  grade_groups = list("ANY" = levels(df$AETOXGR))
)

a_count_occurrences_by_grade(
  df,
  .N_col = 10L,
  .N_row = 10L,
  .var = "AETOXGR",
  id = "USUBJID",
  grade_groups = list("ANY" = levels(df$AETOXGR))
)

Description

The analyze function count_patients_with_event() creates a layout element to calculate patient counts for a user-specified set of events.

This function analyzes primary analysis variable vars which indicates unique subject identifiers. Events are defined by the user as a named vector via the filters argument, where each name corresponds to a variable and each value is the value(s) that that variable takes for the event.

If there are multiple records with the same event recorded for a patient, only one occurrence is counted.

Usage

count_patients_with_event(
  lyt,
  vars,
  filters,
  riskdiff = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = "count_fraction",
  .formats = list(count_fraction = format_count_fraction_fixed_dp),
  .labels = NULL,
  .indent_mods = NULL
)

s_count_patients_with_event(
  df,
  .var,
  filters,
  .N_col,
  .N_row,
  denom = c("n", "N_col", "N_row")
)

a_count_patients_with_event(
  df,
  labelstr = "",
  filters,
  denom = c("n", "N_col", "N_row"),
  .N_col,
  .N_row,
  .df_row,
  .var = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na_str = default_na_str()
)

Arguments

Value

• count_patients_with_event() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_event() to the table layout.

• s_count_patients_with_event() returns the count and fraction of unique identifiers with the defined event.

• a_count_patients_with_event() returns the corresponding list with formatted rtables::CellValue().

Functions

• count_patients_with_event(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• s_count_patients_with_event(): Statistics function which counts the number of patients for which the defined event has occurred.

• a_count_patients_with_event(): Formatted analysis function which is used as afun in count_patients_with_event().

See Also

count_patients_with_flags()

Examples

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_values(
    "STUDYID",
    values = "AB12345",
    .stats = "count",
    .labels = c(count = "Total AEs")
  ) %>%
  count_patients_with_event(
    "SUBJID",
    filters = c("TRTEMFL" = "Y"),
    .labels = c(count_fraction = "Total number of patients with at least one adverse event"),
    table_names = "tbl_all"
  ) %>%
  count_patients_with_event(
    "SUBJID",
    filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
    .labels = c(count_fraction = "Total number of patients with fatal AEs"),
    table_names = "tbl_fatal"
  ) %>%
  count_patients_with_event(
    "SUBJID",
    filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL", "AEREL" = "Y"),
    .labels = c(count_fraction = "Total number of patients with related fatal AEs"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "tbl_rel_fatal"
  )

build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl)

s_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y")
)

s_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL")
)

s_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y", "AEOUT" = "FATAL"),
  denom = "N_col",
  .N_col = 456
)

a_count_patients_with_event(
  tern_ex_adae,
  .var = "SUBJID",
  filters = c("TRTEMFL" = "Y"),
  .N_col = 100,
  .N_row = 100
)

Description

The analyze function count_patients_with_flags() creates a layout element to calculate counts of patients for which user-specified flags are present.

This function analyzes primary analysis variable var which indicates unique subject identifiers. Flags variables to analyze are specified by the user via the flag_variables argument, and must either take value TRUE (flag present) or FALSE (flag absent) for each record.

If there are multiple records with the same flag present for a patient, only one occurrence is counted.

Usage

count_patients_with_flags(
  lyt,
  var,
  flag_variables,
  flag_labels = NULL,
  var_labels = var,
  show_labels = "hidden",
  riskdiff = FALSE,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = paste0("tbl_flags_", var),
  .stats = "count_fraction",
  .formats = list(count_fraction = format_count_fraction_fixed_dp),
  .indent_mods = NULL,
  .labels = NULL
)

s_count_patients_with_flags(
  df,
  .var,
  flag_variables,
  flag_labels = NULL,
  .N_col,
  .N_row,
  denom = c("n", "N_col", "N_row")
)

a_count_patients_with_flags(
  df,
  labelstr = "",
  flag_variables,
  flag_labels = NULL,
  denom = c("n", "N_col", "N_row"),
  .N_col,
  .N_row,
  .df_row,
  .var = NULL,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL,
  na_str = default_na_str()
)

Arguments

Value

• count_patients_with_flags() returns a layout object suitable for passing to further layouting functions, or to rtables::build_table(). Adding this function to an rtable layout will add formatted rows containing the statistics from s_count_patients_with_flags() to the table layout.

• s_count_patients_with_flags() returns the count and the fraction of unique identifiers with each particular flag as a list of statistics n, count, count_fraction, and n_blq, with one element per flag.

• a_count_patients_with_flags() returns the corresponding list with formatted rtables::CellValue().

Functions

• count_patients_with_flags(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().

• s_count_patients_with_flags(): Statistics function which counts the number of patients for which a particular flag variable is TRUE.

• a_count_patients_with_flags(): Formatted analysis function which is used as afun in count_patients_with_flags().

Note

If flag_labels is not specified, variables labels will be extracted from df. If variables are not labeled, variable names will be used instead. Alternatively, a named vector can be supplied to flag_variables such that within each name-value pair the name corresponds to the variable name and the value is the label to use for this variable.

See Also

count_patients_with_event

Examples

# Add labelled flag variables to analysis dataset.
adae <- tern_ex_adae %>%
  dplyr::mutate(
    fl1 = TRUE %>% with_label("Total AEs"),
    fl2 = (TRTEMFL == "Y") %>%
      with_label("Total number of patients with at least one adverse event"),
    fl3 = (TRTEMFL == "Y" & AEOUT == "FATAL") %>%
      with_label("Total number of patients with fatal AEs"),
    fl4 = (TRTEMFL == "Y" & AEOUT == "FATAL" & AEREL == "Y") %>%
      with_label("Total number of patients with related fatal AEs")
  )

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_patients_with_flags(
    "SUBJID",
    flag_variables = c("fl1", "fl2", "fl3", "fl4"),
    denom = "N_col"
  )

build_table(lyt, adae, alt_counts_df = tern_ex_adsl)

# `s_count_patients_with_flags()`

s_count_patients_with_flags(
  adae,
  "SUBJID",
  flag_variables = c("fl1", "fl2", "fl3", "fl4"),
  denom = "N_col",
  .N_col = 1000
)

a_count_patients_with_flags(
  adae,
  .N_col = 10L,
  .N_row = 10L,
  .var = "USUBJID",
  flag_variables = c("fl1", "fl2", "fl3", "fl4")
)

Description

The analyze function count_values() creates a layout element to calculate counts of specific values within a variable of interest.

This function analyzes one or more variables of interest supplied as a vector to vars. Values to count for variable(s) in vars can be given as a vector via the values argument. One row of counts will be generated for each variable.

Usage

count_values(
  lyt,
  vars,
  values,
  na_str = default_na_str(),
  nested = TRUE,
  ...,
  table_names = vars,
  .stats = "count_fraction",
  .formats = NULL,
  .labels = c(count_fraction = paste(values, collapse = ", ")),
  .indent_mods = NULL
)

s_count_values(
  x,
  values,
  na.rm = TRUE,
  .N_col,
  .N_row,
  denom = c("n", "N_col", "N_row")
)

## S3 method for class 'character'
s_count_values(x, values = "Y", na.rm = TRUE, ...)

## S3 method for class 'factor'
s_count_values(x, values = "Y", ...)

## S3 method for class 'logical'
s_count_values(x, values = TRUE, ...)

a_count_values(
  x,
  values,
  na.rm = TRUE,
  .N_col,
  .N_row,
  denom = c("n", "N_col", "N_row")
)

Arguments