Causal mediation analysis for time-to-event outcomes on the Restricted Mean Survival Time scale: A pseudo-value approach

Causal mediation analysis decomposes the total effect of an exposure on an outcome into: 1. the indirect effect through a mediator and 2. the remaining "direct" effect through all other pathways. When the outcome is a time-to-event/survival time, censoring makes identifying the indirect and direct effects on the expected value scale untenable. We propose a semi-parametric estimator of the indirect and direct effects on the restricted mean survival time (RMST) scale using the pseudo-value approach for estimating conditional RMSTs. The pseudo-value approach is generalizable to various forms of outcome censoring. We demonstrate the use of the pseudo-value based estimator to right and interval censored data. Our estimator applies to any set of identification assumptions that lead to the Mediation Formula, including natural, organic, randomized and separable indirect and direct effects. A simulation study demonstrates the performance of the estimators for right and interval censored outcomes under various scenarios. The methodology is applied to an HIV cure example with the intention of estimating the indirect effect of a putative treatment on time-to-viral rebound mediated through the viral reservoir.