ARD Hedge's G Test

Analysis results data for paired and non-paired Hedge's G Effect Size Test using effectsize::hedges_g().

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

ard_effectsize_paired_hedges_g(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 variable. 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::hedges_g(...)

id

(tidy-select)
column name of the subject or participant ID

Value

ARD data frame

Details

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

For the ard_effectsize_paired_hedges_g() 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::hedges_g(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_hedges_g(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…      -0.2
#> 4    ARM      AGE effectsi…   conf.high  CI Upper…     0.399
#> 5    ARM      AGE effectsi…      method     method Hedge's G
#> 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_hedges_g(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…     0.068
#> 2    ARM      AGE effectsi…  conf.level  CI Confi…      0.95
#> 3    ARM      AGE effectsi…    conf.low  CI Lower…    -0.144
#> 4    ARM      AGE effectsi…   conf.high  CI Upper…      0.28
#> 5    ARM      AGE effectsi…      method     method Paired H…
#> 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