Cumulative Counts with Thresholds

Usage

h_count_cumulative(
  x,
  threshold,
  lower_tail = TRUE,
  include_eq = TRUE,
  na.rm = TRUE,
  .N_col
)

s_count_cumulative(
  x,
  thresholds,
  lower_tail = TRUE,
  include_eq = TRUE,
  .N_col,
  ...
)

a_count_cumulative(
  x,
  thresholds,
  lower_tail = TRUE,
  include_eq = TRUE,
  .N_col,
  ...
)

count_cumulative(
  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.

threshold

(number)
a cutoff value as threshold to count values of x.

lower_tail

(logical)
whether to count lower tail, default is TRUE.

include_eq

(logical)
whether to include value equal to the threshold in count, default is TRUE.

na.rm

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

.N_col

(count)
denominator for fraction calculation.

thresholds

(numeric)
vector of cutoff value for the counts.

...

additional arguments for the lower level functions.

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".

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

A named vector of 2:

• count: the count of values less than, less or equal to, greater than, or greater or equal to a threshold of user specification.

• fraction: the fraction of the count.

A named list:

• count_fraction: a list with each thresholds value as a component, each component contains a vector for the count and fraction.

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

Functions

• h_count_cumulative(): Helper function to calculate count and fraction of x values in the lower or upper tail given a threshold.

• s_count_cumulative(): Statistics function that produces a named lists given a (numeric) vector of thresholds.

• a_count_cumulative(): Formatted Analysis function which can be further customized by calling rtables::make_afun() on it. It is used as afun in rtables::analyze().

• count_cumulative(): Layout creating function which can be be used for creating summary tables for cumulative counts of a variable. The ellipsis (...) conveys arguments to s_count_cumulative(), for instance lower_tail = FALSE if upper tail should be accounted for.

Examples

set.seed(1, kind = "Mersenne-Twister")
x <- c(sample(1:10, 10), NA)
.N_col <- length(x)
h_count_cumulative(x, 5, .N_col = .N_col)
#>     count  fraction 
#> 5.0000000 0.4545455 
h_count_cumulative(x, 5, lower_tail = FALSE, include_eq = FALSE, na.rm = FALSE, .N_col = .N_col)
#>     count  fraction 
#> 6.0000000 0.5454545 
h_count_cumulative(x, 0, lower_tail = FALSE, .N_col = .N_col)
#>      count   fraction 
#> 10.0000000  0.9090909 
h_count_cumulative(x, 100, lower_tail = FALSE, .N_col = .N_col)
#>    count fraction 
#>        0        0 
# Internal function - s_count_cumulative
if (FALSE) {
s_count_cumulative(x, thresholds = c(0, 5, 11), .N_col = .N_col)
s_count_cumulative(x, thresholds = c(0, 5, 11), include_eq = FALSE, na.rm = FALSE, .N_col = .N_col)
}

# Internal function - a_count_cumulative
if (FALSE) {
# Use the Formatted Analysis function for `analyze()`. 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_cumulative, .ungroup_stats = "count_fraction")
afun(x, thresholds = c(0, 5, 11), .N_col = .N_col)
}

library(scda)
ADSL <- synthetic_cdisc_data("latest")$adsl
basic_table() %>%
  split_cols_by("ARM") %>%
  add_colcounts() %>%
  count_cumulative(
    vars = "AGE",
    thresholds = c(40, 60)
  ) %>%
  build_table(ADSL)
#>            A: Drug X    B: Placebo    C: Combination
#>             (N=134)       (N=134)        (N=132)    
#> ————————————————————————————————————————————————————
#> AGE                                                 
#>   <= 40   113 (84.3%)   103 (76.9%)    106 (80.3%)  
#>   <= 60   134 (100%)    133 (99.3%)    130 (98.5%)