All Times EDT
Keywords: surrogate endpoints, Bayesian, subgroups, principal stratification, repeated measures, cross-over
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. We extend causal inference approaches to validate such a candidate surrogate using potential outcomes. Using principal stratification, the causal association paradigm assesses the relationship of the treatment effect on the surrogate with the treatment effect on the true endpoint by modeling the joint distribution of the potential outcomes T, given the potential outcomes S. Let S(z) and T(z) refer to the endpoint values had the treatment, possibly counter-factually, been assigned to level z. We build upon previous models for the joint distribution of potential outcomes that assume multivariate normality among the endpoints under a binary treatment. Our setting of interest allows us to assume the surrogate under the placebo, S(0), is zero-valued, and we incorporate baseline covariates. Conditioning on covariates may improve the plausibility of conditional independence assumptions, and it allows us to make inference about whether there are subgroups of subjects for whom the quality of the surrogate varies. We also propose incorporating longitudinal measures of T using mixed models. 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 where the primary outcome is a continuous functional score. Since muscular growth and deterioration from disease have major impact on mobility, baseline ambulatory ability, time, and age are important to take into consideration to evaluate surrogacy. The trial of interest will also include a cross-over portion where placebo subjects receive the experimental treatment mid-trial, and we consider modeling these additional endpoints in the validation framework.