Randomized interventional effects in semicompeting risks

Abstract: In clinical studies, the risk of the primary (terminal) event may be modified by intermediate events, resulting in semicompeting risks. To study the treatment effect on the terminal event mediated by the intermediate event, researchers wish to decompose the total effect into direct and indirect effects. In this article, we extend the randomized interventional approach to time-to-event data, where both the intermediate and terminal events are subject to right censoring. We envision a random draw for the intermediate event process according to some reference distribution, either marginally over time-varying confounders or conditionally given observed history. We present the identification formula for interventional effects and discuss some variants of the identification assumptions. The target estimands can be estimated using likelihood-based methods. As an illustration, we study the effect of transplant modalities on death mediated by relapse in an allogeneic stem cell transplantation study to treat leukemia with a time-varying confounder.