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Activity Number: 187 - Surrogate Markers and the Role of Mediation Analysis in Drug Development
Type: Invited
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Biometrics Section
Abstract #316826
Title: Joint Modeling of Surrogate and Primary End Points via Causal Mediation Analyses
Author(s): Yen-Tsung Huang*
Companies: Academia Sinica
Keywords: causal inference; causal mediation model; Nelson-Aalen estimator; semi-competing risks; surrogate end point

In clinical trials, one may be interested in the treatment effect on both the surrogate end point (i.e., an intermediate event) and the primary end point (i.e., a terminal event). The analyses of both events constitute a semi-competing risks problem where the surrogate end point may be censored by the primary end point, but not vice versa. Here we propose a nonparametric approach casting the semi-competing risks problem in the framework of causal mediation modeling. We set up a mediation model with the intermediate and terminal events, respectively as the mediator and the outcome, and define indirect effect as the effect of the exposure on the primary event mediated by the intermediate event and direct effect as that not mediated by the intermediate event. A nonparametric estimator is proposed for direct and indirect effects, which can be viewed as a Nelson-Aalen estimator with time-varying weights. Theoretical properties such as asymptotic unbiasedness, consistency and asymptotic normality are established for the proposed estimator. Numerical simulation and data application are presented to illustrate the finite sample performance and utility of the proposed method.

Authors who are presenting talks have a * after their name.

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