ARD Wilcoxon Rank-Sum Test — ard_stats_wilcox

Analysis results data for paired and non-paired Wilcoxon Rank-Sum tests.

Usage

ard_stats_wilcox_test(data, variables, by = NULL, conf.level = 0.95, ...)

ard_stats_paired_wilcox_test(data, by, variables, id, conf.level = 0.95, ...)

Arguments

data: (data.frame)
a data frame. See below for details.
variables: (tidy-select)
column names to be compared. Independent tests will be computed for each variable.
by: (tidy-select)
optional column name to compare by.
conf.level: (scalar numeric)
confidence level for confidence interval. Default is 0.95.
...: arguments passed to wilcox.test(...)
id: (tidy-select)
column name of the subject or participant ID.

Value

ARD data frame

Details

For the ard_stats_wilcox_test() function, the data is expected to be one row per subject. The data is passed as wilcox.test(data[[variable]] ~ data[[by]], paired = FALSE, ...).

For the ard_stats_paired_wilcox_test() function, the data is expected to be one row per subject per by level. Before the test is calculated, the data are reshaped to a wide format to be one row per subject. The data are then passed as wilcox.test(x = data_wide[[<by level 1>]], y = data_wide[[<by level 2>]], paired = TRUE, ...).

Examples

cards::ADSL |>
  dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
  ard_stats_wilcox_test(by = "ARM", variables = "AGE")
#> {cards} data frame: 12 x 9
#>    group1 variable   context   stat_name stat_label      stat
#> 1     ARM      AGE stats_wi…   statistic  X-square…    3862.5
#> 2     ARM      AGE stats_wi…     p.value    p-value     0.435
#> 3     ARM      AGE stats_wi…      method     method Wilcoxon…
#> 4     ARM      AGE stats_wi… alternative  alternat… two.sided
#> 5     ARM      AGE stats_wi…          mu         mu         0
#> 6     ARM      AGE stats_wi…      paired  Paired t…     FALSE
#> 7     ARM      AGE stats_wi…       exact      exact          
#> 8     ARM      AGE stats_wi…     correct    correct      TRUE
#> 9     ARM      AGE stats_wi…    conf.int   conf.int     FALSE
#> 10    ARM      AGE stats_wi…  conf.level  CI Confi…      0.95
#> 11    ARM      AGE stats_wi…    tol.root   tol.root         0
#> 12    ARM      AGE stats_wi… digits.rank  digits.r…       Inf
#> ℹ 3 more variables: fmt_fn, warning, error

# constructing a paired data set,
# where patients receive both treatments
cards::ADSL[c("ARM", "AGE")] |>
  dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
  dplyr::mutate(.by = ARM, USUBJID = dplyr::row_number()) |>
  dplyr::arrange(USUBJID, ARM) |>
  ard_stats_paired_wilcox_test(by = ARM, variables = AGE, id = USUBJID)
#> {cards} data frame: 12 x 9
#>    group1 variable   context   stat_name stat_label      stat
#> 1     ARM      AGE stats_wi…   statistic  X-square…      1754
#> 2     ARM      AGE stats_wi…     p.value    p-value     0.522
#> 3     ARM      AGE stats_wi…      method     method Wilcoxon…
#> 4     ARM      AGE stats_wi… alternative  alternat… two.sided
#> 5     ARM      AGE stats_wi…          mu         mu         0
#> 6     ARM      AGE stats_wi…      paired  Paired t…      TRUE
#> 7     ARM      AGE stats_wi…       exact      exact          
#> 8     ARM      AGE stats_wi…     correct    correct      TRUE
#> 9     ARM      AGE stats_wi…    conf.int   conf.int     FALSE
#> 10    ARM      AGE stats_wi…  conf.level  CI Confi…      0.95
#> 11    ARM      AGE stats_wi…    tol.root   tol.root         0
#> 12    ARM      AGE stats_wi… digits.rank  digits.r…       Inf
#> ℹ 3 more variables: fmt_fn, warning, error