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)
#> # An ARD data frame: 9 × 9
#>   group1 variable context stat_name stat_label stat       fmt_fun warning error 
#>   <chr>  <chr>    <chr>   <chr>     <chr>      <named li> <named> <named> <name>
#> 1 ARM    AGE      effect… estimate  Effect Si… 0.1004081  1       <NULL>  <NULL>
#> 2 ARM    AGE      effect… conf.lev… CI Confid… 0.95       1       <NULL>  <NULL>
#> 3 ARM    AGE      effect… conf.low  CI Lower … -0.2005979 1       <NULL>  <NULL>
#> 4 ARM    AGE      effect… conf.high CI Upper … 0.4011158  1       <NULL>  <NULL>
#> 5 ARM    AGE      effect… method    method     Cohen's D  <NULL>  <NULL>  <NULL>
#> 6 ARM    AGE      effect… mu        H0 Mean    0          1       <NULL>  <NULL>
#> 7 ARM    AGE      effect… paired    Paired te… FALSE      <NULL>  <NULL>  <NULL>
#> 8 ARM    AGE      effect… pooled_sd Pooled St… TRUE       <NULL>  <NULL>  <NULL>
#> 9 ARM    AGE      effect… alternat… Alternati… two.sided  <NULL>  <NULL>  <NULL>

# 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)
#> # An ARD data frame: 9 × 9
#>   group1 variable context          stat_name stat_label stat             fmt_fun
#>   <chr>  <chr>    <chr>            <chr>     <chr>      <named list>     <named>
#> 1 ARM    AGE      effectsize_cohe… estimate  Effect Si… 0.06857301       1      
#> 2 ARM    AGE      effectsize_cohe… conf.lev… CI Confid… 0.95             1      
#> 3 ARM    AGE      effectsize_cohe… conf.low  CI Lower … -0.1457359       1      
#> 4 ARM    AGE      effectsize_cohe… conf.high CI Upper … 0.2824702        1      
#> 5 ARM    AGE      effectsize_cohe… method    method     Paired Cohen's D <NULL> 
#> 6 ARM    AGE      effectsize_cohe… mu        H0 Mean    0                1      
#> 7 ARM    AGE      effectsize_cohe… paired    Paired te… TRUE             <NULL> 
#> 8 ARM    AGE      effectsize_cohe… pooled_sd Pooled St… TRUE             <NULL> 
#> 9 ARM    AGE      effectsize_cohe… alternat… Alternati… two.sided        <NULL> 
#> # ℹ 2 more variables: warning <named list>, error <named list>