Marginal Time-Dependent Causal Effects in Mediation Analysis With Survival Data

The main aim of mediation analysis is to study the direct and indirect effects of an exposure on an outcome. To date, the literature on mediation analysis with multiple mediators has mainly focused on continuous and dichotomous outcomes. However, the development of methods for multiple mediation analysis of survival outcome is still limited. Here we extend to survival outcomes a method for multiple mediation analysis based on the computation of appropriate weights. The approach considered has the advantage of not requiring specific models for mediators, allowing non-independent mediators of any nature and not relying on the assumption of rare outcomes. Simulation studies show a good performance of the proposed estimator, in terms of bias and coverage probability. The method is further applied to an example from a published study on mortality for prostate cancer aimed at understanding to what extent the effect of DNA methyltransferase genotype on mortality was explained by DNA methylation and tumor aggressiveness. The approach can be used to quantify the marginal time-dependent direct and indirect effects carried by multiple indirect pathways and a code is provided to facilitate its application.