G-computation Analysis for a Single Visit

Performs logistic regression and estimates marginal effects for binary outcomes.

Usage

gcomp_responder(
  data,
  vars,
  reference_levels = NULL,
  var_method = "Ge",
  type = "HC0",
  contrast = "diff"
)

Arguments

data

A data.frame with one visit of data.

vars

A list containing group, outcome, covariates, and visit.

reference_levels

Optional vector specifying reference level(s) of the treatment factor.

var_method

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

type

Type of robust variance estimator (default: "HC0").

contrast

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

Value

A named list containing estimates and standard errors for treatment comparisons and within-arm means.

Examples

# \donttest{
library(dplyr)
library(rbmi)
library(rbmiUtils)

data("ADMI")

# Prepare data for a single visit
ADMI <- ADMI |>
  mutate(
    TRT = factor(TRT, levels = c("Placebo", "Drug A")),
    STRATA = factor(STRATA),
    REGION = factor(REGION)
  )

dat_single <- ADMI |>
  filter(AVISIT == "Week 24")

vars <- set_vars(
  subjid = "USUBJID",
  visit = "AVISIT",
  group = "TRT",
  outcome = "CRIT1FLN",
  covariates = c("BASE", "STRATA", "REGION")
)

result <- gcomp_responder(
  data = dat_single,
  vars = vars,
  reference_levels = "Placebo"
)

print(result)
#> $`trt_Drug A-Placebo`
#> $`trt_Drug A-Placebo`$est
#> [1] -0.03081266
#> 
#> $`trt_Drug A-Placebo`$se
#> [1] 0.001122321
#> 
#> $`trt_Drug A-Placebo`$df
#> [1] NA
#> 
#> 
#> $`lsm_Drug A`
#> $`lsm_Drug A`$est
#> [1] 7.701861e-05
#> 
#> $`lsm_Drug A`$se
#> [1] 5.448088e-05
#> 
#> $`lsm_Drug A`$df
#> [1] NA
#> 
#> 
#> $lsm_Placebo
#> $lsm_Placebo$est
#> [1] 0.03088968
#> 
#> $lsm_Placebo$se
#> [1] 0.001120489
#> 
#> $lsm_Placebo$df
#> [1] NA
#> 
#> 
# }