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Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm
Journal article   Peer reviewed

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

Emily K. Roberts, Michael R. Elliott and Jeremy M. G. Taylor
Statistics in medicine, Vol.40(29), pp.6605-6618
12/20/2021
DOI: 10.1002/sim.9201
PMID: 34528260
url
https://arxiv.org/pdf/2104.12947View
Open Access

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.
 Life Sciences & Biomedicine Mathematical & Computational Biology Mathematics Medical Informatics Medicine, Research & Experimental Physical Sciences Public, Environmental & Occupational Health Research & Experimental Medicine Science & Technology Statistics & Probability

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