Estimation of Interventional Effects in Clinical Trials With a Repeatedly Measured Mediator

Causal mediation analysis is an important tool in medical research, enabling researchers to examine and quantify the mechanisms through which a treatment exerts its effects on an outcome of interest. In many studies, the potential mediator is measured repeatedly over time, and we expect feedback between other post-treatment covariates and the mediator. Randomized interventional (also called stochastic) effects have been proposed for defining causal mediation estimands in longitudinal settings, because they are identifiable in the presence of post-treatment mediator-outcome confounders. In this article, we use the interventional effects framework to investigate the mediating pathways in a clinical trial that compared the effect of semaglutide versus placebo on the histological resolution of nonalcoholic steatohepatitis (NASH). We define and identify various interventional direct and indirect effects relevant to the research question. For estimation, we propose an efficient and multiply robust Targeted Minimum Loss Estimator (TMLE), and we showcase the theoretical properties of the estimator in a simulation study.