Between-case standardized mean difference for replicated AB designs

Calculates a case-adjusted standardized mean difference for replicated AB single-case data and an approximate sampling variance. The estimator is a small-sample corrected intervention coefficient from a linear model with case fixed effects, divided by its residual standard deviation.

Usage

bc_smd_computation(
  data,
  studyID,
  subjectID,
  outcome_name,
  phase_name,
  phase_order = NULL,
  improvement = c("increase", "decrease"),
  na_option = "listwise"
)

Arguments

data

Data frame containing repeated observations.

studyID, subjectID, outcome_name, phase_name

Character strings naming the study, case, outcome, and phase columns.

phase_order

Character vector giving the baseline and intervention labels, in that order. If NULL, factor-level or alphabetical order is used.

improvement

Whether an increase or decrease in the outcome is beneficial.

na_option

Currently only "listwise" is supported.

Value

A data frame with one row per study. yi is the Hedges-style small-sample corrected case-adjusted standardized mean difference and vi is its delta-method sampling-variance approximation.

Details

This initial implementation supports replicated AB data only: every study must contain at least two cases and every case must contribute baseline and intervention observations. It assumes independent residuals conditional on case and phase. It is not a replacement for a design-specific mixed-model BC-SMD for complex multiple-baseline, reversal, or serially correlated data.

Examples

dat <- data.frame(
  study = rep("S1", 12), case = rep(c("A", "B"), each = 6),
  phase = rep(rep(c("baseline", "intervention"), each = 3), 2),
  outcome = c(1, 2, 2, 4, 5, 5, 2, 2, 3, 5, 5, 6)
)
bc_smd_computation(dat, "study", "case", "outcome", "phase")
#>    study_id case_id effect_id metric      yi       vi n_baseline n_intervention
#> S1       S1    <NA>    BC_SMD BC-SMD 5.03895 1.689252          6              6
#>    residual_df
#> S1           9