count_missed_doses: Count number of patients with missed doses by thresholds

Description

The analyze function creates a layout element to calculate cumulative counts of patients with number of missed doses at least equal to user-specified threshold values.

This function analyzes numeric variable vars, a variable with numbers of missed doses, against the threshold values supplied to the thresholds argument as a numeric vector. This function assumes that every row of the given data frame corresponds to a unique patient.

Usage

count_missed_doses(
  lyt,
  vars,
  thresholds,
  var_labels = vars,
  show_labels = "visible",
  na_str = default_na_str(),
  nested = TRUE,
  table_names = vars,
  ...,
  na_rm = TRUE,
  .stats = c("n", "count_fraction"),
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)
s_count_missed_doses(
  x,
  thresholds,
  .N_col,
  .N_row,
  denom = c("N_col", "n", "N_row"),
  ...
)
a_count_missed_doses(
  x,
  ...,
  .stats = NULL,
  .stat_names = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Value

count_missed_doses() 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_missed_doses() to the table layout.

s_count_missed_doses() returns the statistics n and count_fraction with one element for each threshold.

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

Arguments

lyt

(PreDataTableLayouts)
layout that analyses will be added to.

vars

(character)
variable names for the primary analysis variable to be iterated over.

thresholds

(numeric)
minimum number of missed doses the patients had.

var_labels

(character)
variable labels.

show_labels

(string)
label visibility: one of "default", "visible" and "hidden".

na_str

(string)
string used to replace all NA or empty values in the output.

nested

(flag)
whether this layout instruction should be applied within the existing layout structure _if possible (TRUE, the default) or as a new top-level element (FALSE). Ignored if it would nest a split. underneath analyses, which is not allowed.

table_names

(character)
this can be customized in the case that the same vars are analyzed multiple times, to avoid warnings from rtables.

...

additional arguments for the lower level functions.

na_rm

(flag)
whether NA values should be removed from x prior to analysis.

.stats

(character)
statistics to select for the table.

Options are: 'n', 'count_fraction'

.stat_names

(character)
names of the statistics that are passed directly to name single statistics (.stats). This option is visible when producing rtables::as_result_df() with make_ard = TRUE.

.formats

(named character or list)
formats for the statistics. See Details in analyze_vars for more information on the "auto" setting.

.labels

(named character)
labels for the statistics (without indent).

.indent_mods

(named integer)
indent modifiers for the labels. Defaults to 0, which corresponds to the unmodified default behavior. Can be negative.

x

(numeric)
vector of numbers we want to analyze.

.N_col

(integer(1))
column-wise N (column count) for the full column being analyzed that is typically passed by rtables.

.N_row

(integer(1))
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is typically passed by rtables.

denom

(string)
choice of denominator for proportion. Options are:

n: number of values in this row and column intersection.
N_row: total number of values in this row across columns.
N_col: total number of values in this column across rows.

Functions

count_missed_doses(): Layout-creating function which can take statistics function arguments and additional format arguments. This function is a wrapper for rtables::analyze().
s_count_missed_doses(): Statistics function to count patients with missed doses.
a_count_missed_doses(): Formatted analysis function which is used as afun in count_missed_doses().

Examples

Run this code

library(dplyr)

anl <- tern_ex_adsl %>%
  distinct(STUDYID, USUBJID, ARM) %>%
  mutate(
    PARAMCD = "TNDOSMIS",
    PARAM = "Total number of missed doses during study",
    AVAL = sample(0:20, size = nrow(tern_ex_adsl), replace = TRUE),
    AVALC = ""
  )

basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_missed_doses("AVAL", thresholds = c(1, 5, 10, 15), var_labels = "Missed Doses") %>%
  build_table(anl, alt_counts_df = tern_ex_adsl)