Model-based causal mediation analysis — causalMediation • PhysioClinStats

Calls mediation::mediate() after checking that the mediator and outcome models describe the same ordered observations. Estimates rely on sequential ignorability, consistency, positivity, absence of a treatment-induced mediator-outcome confounder, and correct mediator and outcome models. An indirect association, even when statistically significant, is not evidence that a biological mechanism has been established.

Usage

causalMediation(
  model_m,
  model_y,
  treat,
  mediator,
  control.value = 0,
  treat.value = 1,
  covariates = NULL,
  sims = 1000L,
  boot = FALSE,
  boot.ci.type = "perc",
  conf.level = 0.95,
  robustSE = FALSE,
  sensitivity = TRUE,
  seed = NULL,
  ...
)

Arguments

model_m, model_y: Supported fitted mediator and outcome models, or AnalysisResult objects whose result$fit contains those models.
treat, mediator: Treatment and mediator variable names.
control.value, treat.value: Treatment contrast supplied to the backend.
covariates: Optional named list fixing covariates for a conditional mediation estimand.
sims: Positive integer number of simulations or bootstrap replicates.
boot: Use the nonparametric bootstrap.
boot.ci.type: Bootstrap confidence-interval type.
conf.level: Confidence level in (0, 1).
robustSE: Request heteroskedasticity-consistent uncertainty for supported lm/glm models.
sensitivity: Run mediation::medsens() for an all-linear model pair.
seed: Optional integer seed. The caller's global RNG state is restored.
...: Further arguments passed to mediation::mediate().

Value

An AnalysisResult with a complete ACME/ADE/total/proportion table, the backend object, settings, formulas, assumptions, and sensitivity diagnostics.