Counting Missed Doses — count_missed

These are specific functions to count patients with missed doses. The difference to count_cumulative() is mainly the special labels.

Usage

s_count_nonmissing(x)

d_count_missed_doses(thresholds)

s_count_missed_doses(x, thresholds, .N_col)

a_count_missed_doses(x, thresholds, .N_col)

count_missed_doses(
  lyt,
  vars,
  var_labels = vars,
  show_labels = "visible",
  ...,
  table_names = vars,
  .stats = NULL,
  .formats = NULL,
  .labels = NULL,
  .indent_mods = NULL
)

Arguments

x: (numeric)
vector of numbers we want to analyze.
thresholds: (vector of count)
number of missed doses the patients at least had.
.N_col: (count)
row-wise N (row group count) for the group of observations being analyzed (i.e. with no column-based subsetting) that is passed by rtables.
lyt: (layout)
input layout where analyses will be added to.
vars: (character)
variable names for the primary analysis variable to be iterated over.
var_labels: character for label.
show_labels: label visibility: one of "default", "visible" and "hidden".
...: additional arguments for the lower level functions.
table_names: (character)
this can be customized in case that the same vars are analyzed multiple times, to avoid warnings from rtables.
.stats: (character)
statistics to select for the table.
.formats: (named character or list)
formats for the statistics.
.labels: (named character)
labels for the statistics (without indent).
.indent_mods: (named integer)
indent modifiers for the labels.

Value

s_count_nonmissing() returns the statistic n which is the count of non-missing values in x.

d_count_missed_doses() returns a named character vector with the labels.

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

Functions

s_count_nonmissing(): Statistics function to count non-missing values.
d_count_missed_doses(): Description function that calculates labels for s_count_missed_doses().
s_count_missed_doses(): Statistics function to count patients with missed doses when x is the vector of number of missed doses with one value for each patient.
a_count_missed_doses(): Formatted Analysis function to count non-missing values.
count_missed_doses(): Layout creating function which can be be used for creating summary tables for summarizing missed doses given user-specified thresholds. This is an additional layer on top of count_cumulative specifically for missed doses.

Examples

set.seed(1)
x <- c(sample(1:10, 10), NA)

# Internal function - s_count_nonmissing
if (FALSE) {
s_count_nonmissing(x)
}

# Internal function - s_count_missed_doses
if (FALSE) {
s_count_missed_doses(x = c(0, 1, 0, 2, 3, 4, 0, 2), thresholds = c(2, 5), .N_col = 10)
}

# Internal function - a_count_missed_doses
if (FALSE) {
#  We need to ungroup `count_fraction` first so that the `rtables` formatting
# function `format_count_fraction()` can be applied correctly.
afun <- make_afun(a_count_missed_doses, .ungroup_stats = "count_fraction")
afun(x = c(0, 1, 0, 2, 3, 4, 0, 2), thresholds = c(2, 5), .N_col = 10)
}

library(dplyr)
library(scda)
adex <- synthetic_cdisc_data("latest")$adex
adsl <- synthetic_cdisc_data("latest")$adsl
anl <- adex %>%
  distinct(STUDYID, USUBJID, ARM) %>%
  mutate(
    PARAMCD = "TNDOSMIS",
    PARAM = "Total number of missed doses during study",
    AVAL = sample(0:20, size = nrow(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 = adsl)
#>                               A: Drug X    B: Placebo    C: Combination
#>                                (N=134)       (N=134)        (N=132)    
#> ———————————————————————————————————————————————————————————————————————
#> Missed Doses                                                           
#>   n                              134           134            132      
#>   At least 1 missed dose     127 (94.8%)   127 (94.8%)    124 (93.9%)  
#>   At least 5 missed doses    102 (76.1%)   105 (78.4%)     98 (74.2%)  
#>   At least 10 missed doses   64 (47.8%)    72 (53.7%)      65 (49.2%)  
#>   At least 15 missed doses   42 (31.3%)    41 (30.6%)      32 (24.2%)