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Abstract:
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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 and the principal surrogacy criteria. 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 S(0), S(1), T(0), T(1) under a binary treatment. We propose incorporating longitudinal measures of T using mixed models. We demonstrate our approach via simulation and data that mimics an ongoing study of a muscular dystrophy gene therapy. Since muscular growth and deterioration from disease have major impact on mobility, both baseline ambulatory ability, measured pre-treatment, 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.
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