Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm

TL;DR

This paper develops Bayesian methods to validate surrogate endpoints in clinical trials by incorporating baseline covariates and potential outcomes, demonstrated through simulation and muscular dystrophy data.

Contribution

It introduces a novel Bayesian framework that accounts for baseline covariates and potential outcomes to assess surrogate validity under a constant biomarker assumption.

Findings

Bayesian methods effectively validate surrogates in simulated data.

Incorporating covariates improves surrogacy assessment accuracy.

Application to muscular dystrophy data demonstrates practical utility.

Abstract

A surrogate endpoint S in a clinical trial is an outcome that may be measured earlier or more easily than the true outcome of interest T. In this work, we extend causal inference approaches to validate such a surrogate using potential outcomes. The causal association paradigm assesses the relationship of the treatment effect on the surrogate with the treatment effect on the true endpoint. Using the principal surrogacy criteria, we utilize the joint conditional distribution of the potential outcomes T, given the potential outcomes S. In particular, our setting of interest allows us to assume the surrogate under the placebo, S(0), is zero-valued, and we incorporate baseline covariates in the setting of normally-distributed endpoints. We develop Bayesian methods to incorporate conditional independence and other modeling assumptions and explore their impact on the assessment of surrogacy. We demonstrate our approach via simulation and data that mimics an ongoing study of a muscular dystrophy gene therapy.