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Incorporating baseline covariates to validate surrogate endpoints with a constant biomarker under control arm
Author(s) -
Roberts Emily K.,
Elliott Michael R.,
Taylor Jeremy M. G.
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9201
Subject(s) - covariate , surrogate endpoint , causal inference , inference , outcome (game theory) , baseline (sea) , conditional independence , computer science , econometrics , statistics , machine learning , artificial intelligence , medicine , mathematics , oceanography , mathematical economics , geology
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.