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Simulated treatment comparison via G-computation

Source: R/stc.R
stc.Rd

Perform an unanchored simulated treatment comparison (STC) using parametric G-computation. Fits a regression on IPD, predicts counterfactual outcomes in both the index and comparator populations, and computes marginal treatment effects with delta-method standard errors.

Usage

stc(data, link = NULL, conf_level = 0.95)

Arguments

data

An mlumr_data object from combine_data(). Integration points are not required for STC but covariate information from the AgD is used.

Link function. For binomial: "logit" (default), "probit", or "cloglog". For normal: "identity" (default) or "log". For poisson: "log" (default). If NULL, uses the canonical default.

conf_level

Confidence level for the interval (default 0.95)

Value

An object of class mlumr_stc

Details

For binomial outcomes, returns the treatment effect on the link scale plus event probabilities, risk difference, and log risk ratio with SEs and CIs for both populations. Event-probability intervals use Wald standard errors and are bounded to [0, 1]. For Poisson outcomes, the comparator log rate uses a 0.5 continuity correction when the observed event count is zero.

The STC procedure is:

  1. Fit a GLM on IPD (binomial/gaussian/poisson as appropriate).

  2. Predict on comparator-population covariates (from integration points or AgD covariate means).

  3. Marginalize predictions over the comparator population.

  4. Predict on index-population covariates (IPD).

  5. Compute treatment effects and SEs via the delta method.

STC is a parametric G-computation benchmark. It relies on the IPD outcome model being correctly specified and transportable to the comparator population. It does not model posterior uncertainty in population covariate distributions or relax treatment-specific covariate effects. When clinically meaningful effect modification is plausible, prefer mlumr(..., model = "relaxed") as the primary analysis and use STC as a sensitivity or benchmarking analysis.

For binomial outcomes, the comparator-population treatment contrast is transported to the index population assuming the treatment contrast is constant on the fitted link scale (i.e., no effect modification on that scale). Under this assumption, the index-population comparator probability is computed as inv_link(link(p_A_index) - (link(p_A_comp) - link(p_B))), and its uncertainty is propagated through the delta method. If effect modification is expected, fit a Bayesian relaxed model with mlumr(..., model = "relaxed") and use predict(..., population = "index") instead, which does not require this assumption.

Examples

if (FALSE) { # \dontrun{
result <- stc(dat)
print(result)
} # }