rbmiUtils (version 0.1.8)

gcomp_binary: Utility function for Generalized G-computation for Binary Outcomes

Description

Wrapper function for targeting a marginal treatment effect using g-computation using the beeca package. Intended for binary endpoints.

Usage

gcomp_binary(
  data,
  outcome = "CRIT1FLN",
  treatment = "TRT",
  covariates = c("BASE", "STRATA", "REGION"),
  reference = "Placebo",
  contrast = "diff",
  method = "Ge",
  type = "HC0",
  ...
)

Value

A named list with treatment effect estimate, standard error, and degrees of freedom (if applicable).

Arguments

data

A data.frame containing the analysis dataset.

outcome

Name of the binary outcome variable (as string).

treatment

Name of the treatment variable (as string).

covariates

Character vector of covariate names to adjust for.

reference

Reference level for the treatment variable (default: "Placebo").

contrast

Type of contrast to compute (default: "diff").

method

Marginal estimation method for variance (default: "Ge").

type

Variance estimator type (default: "HC0").

...

Additional arguments passed to beeca::get_marginal_effect().

Examples

# Load required packages
library(rbmiUtils)
library(beeca)      # for get_marginal_effect()
library(dplyr)
# Load example data
data("ADMI")
# Ensure correct factor levels
ADMI <- ADMI |>
  mutate(
    TRT = factor(TRT, levels = c("Placebo", "Drug A")),
    STRATA = factor(STRATA),
    REGION = factor(REGION)
  )
# Apply g-computation for binary responder
result <- gcomp_binary(
  data = ADMI,
  outcome = "CRIT1FLN",
  treatment = "TRT",
  covariates = c("BASE", "STRATA", "REGION"),
  reference = "Placebo",
  contrast = "diff",
  method = "Ge",    # from beeca: GEE robust sandwich estimator
  type = "HC0"      # from beeca: heteroskedasticity-consistent SE
)

# Print results
print(result)