A Bayesian semiparametric survival method on mediation analysis with dynamic treatment regimes

Music City Center 

The direct and indirect effects of treatments or treatment strategies over time are often of interest in clinical research. For such questions, methodological challenges can include time-dependent confounding, informative timing of treatment decisions, and censoring. To address potential unidentifiability arising from post-treatment confounding and the right censoring, we define the mediator distribution using a random intervention (RI)-based conditional distribution. For modeling the observed data, we employ a generative Bayesian semiparametric survival model with a correlated gamma process hazard. The mediation effect is estimated through Monte Carlo g-computation using posterior draws from a blocked Metropolis-in-Gibbs sampler. We assess the performance of the method using simulated data, focusing on counterfactual effect predictions and mediation effects estimation under hypothetical treatment rules. We apply the method to a pediatric acute myeloid leukemia (AML) study to evaluate mediation effects of dynamic treatment regimens through clinical biomarkers such as organ failure.

Causal mediation analysis

Bayesian survival analysis

dynamic treatment regimes

gamma process

proportional hazards model 

ENAR