ard_effectsize_cohens_d {cardx}

ARD Cohen's D Test

Description

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

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, ...)⁠.

Value

ARD data frame

Examples

cards::ADSL |>
  dplyr::filter(ARM %in% c("Placebo", "Xanomeline High Dose")) |>
  ard_effectsize_cohens_d(by = ARM, variables = AGE)

# 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)