Abstract

The use of surrogate endpoints instead of final endpoints may be attractive for clinical trials. However, before a candidate surrogate can be used in a clinical trial, it must be statistically validated. Joint modeling is a powerful approach to better understand the treatment effect when time-to-event and longitudinal endpoints commonly co-occur in clinical trials.
We focus on the cases where the final endpoint is a time-to-event endpoint (such as time-to-death) and the surrogate is either a time-to-event or a longitudinal biomarker. Two new joint models and different approaches were proposed depending on the nature of the surrogate to validate surrogate endpoints. A mediation analysis can be proposed to decompose the total effect of the treatment on the final endpoint as a direct effect and an indirect effect through the surrogate. The ratio of the indirect effect over the total effect of the treatment on the final endpoint can be computed from the parameters of the model and used as a measure of surrogacy.
We present applications of survival mediation analysis using real oncology datasets, both with and without a meta-analytic structure. The proposed analyses investigate time-to-relapse as a surrogate for overall survival in gastric cancer, and tumor size as a surrogate biomarker for overall survival in colorectal cancer.
The R package frailtypack, available at https://CRAN.R-project.org/package=frailtypack, provides a user-friendly implementation of several methods for validating surrogate biomarkers or surrogate time-to-event endpoints.
We proposed a valuable tool to inform decision-making and advance our understanding of different treatment effects in mediation analysis with either a longitudinal mediator or a time-to-event mediator for a final survival endpoint.