ARD Cohen's D Test — ard_effectsize_cohens

Analysis results data for paired and non-paired Cohen's D Effect Size Test using effectsize::cohens_d().

Usage

ard_effectsize_cohens_d(data, by, variables, conf.level = 0.95, ...)

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

Arguments

data: (data.frame)
a data frame. See below for details.
by: (tidy-select)
column name to compare by. Must be a categorical variable with exactly two levels.
variables: (tidy-select)
column names to be compared. Must be a continuous variables. Independent tests will be run for each variable.
conf.level: (scalar numeric)
confidence level for confidence interval. Default is 0.95.
...: arguments passed to effectsize::cohens_d(...)
id: (tidy-select)
column name of the subject or participant ID

Value

ARD data frame

Details

For the ard_effectsize_cohens_d() function, the data is expected to be one row per subject. The data is passed as effectsize::cohens_d(data[[variable]]~data[[by]], data, paired = FALSE, ...).

For the ard_effectsize_paired_cohens_d() function, the data is expected to be one row per subject per by level. Before the effect size is calculated, the data are reshaped to a wide format to be one row per subject. The data are then passed as effectsize::cohens_d(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_effectsize_cohens_d(by = ARM, variables = AGE)
#> {cards} data frame: 9 x 9
#>   group1 variable   context   stat_name stat_label      stat
#> 1    ARM      AGE effectsi…    estimate  Effect S…       0.1
#> 2    ARM      AGE effectsi…  conf.level  CI Confi…      0.95
#> 3    ARM      AGE effectsi…    conf.low  CI Lower…    -0.201
#> 4    ARM      AGE effectsi…   conf.high  CI Upper…     0.401
#> 5    ARM      AGE effectsi…      method     method Cohen's D
#> 6    ARM      AGE effectsi…          mu    H0 Mean         0
#> 7    ARM      AGE effectsi…      paired  Paired t…     FALSE
#> 8    ARM      AGE effectsi…   pooled_sd  Pooled S…      TRUE
#> 9    ARM      AGE effectsi… alternative  Alternat… two.sided
#> 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) |>
  dplyr::group_by(USUBJID) |>
  dplyr::filter(dplyr::n() > 1) |>
  ard_effectsize_paired_cohens_d(by = ARM, variables = AGE, id = USUBJID)
#> {cards} data frame: 9 x 9
#>   group1 variable   context   stat_name stat_label      stat
#> 1    ARM      AGE effectsi…    estimate  Effect S…     0.069
#> 2    ARM      AGE effectsi…  conf.level  CI Confi…      0.95
#> 3    ARM      AGE effectsi…    conf.low  CI Lower…    -0.146
#> 4    ARM      AGE effectsi…   conf.high  CI Upper…     0.282
#> 5    ARM      AGE effectsi…      method     method Paired C…
#> 6    ARM      AGE effectsi…          mu    H0 Mean         0
#> 7    ARM      AGE effectsi…      paired  Paired t…      TRUE
#> 8    ARM      AGE effectsi…   pooled_sd  Pooled S…      TRUE
#> 9    ARM      AGE effectsi… alternative  Alternat… two.sided
#> ℹ 3 more variables: fmt_fn, warning, error