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Activity Number: 480 - Causal Inference and Optimal Decision-Making
Type: Contributed
Date/Time: Wednesday, August 10, 2022 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #323606
Title: Solutions for Surrogacy Validation with Longitudinal Outcomes for a Gene Therapy
Author(s): Emily Roberts* and Michael Elliott and Jeremy Taylor
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: causal inference; Bayesian; longitudinal; cross-over; surrogate endpoints; clinical trial

Valid surrogate endpoints S can be used as a substitute for a true outcome of interest T to measure treatment efficacy in a clinical trial. We propose a causal inference approach to validate a surrogate by incorporating longitudinal measurements of the true outcomes using a mixed modeling approach, and we define models and quantities for validation that may vary across the study period using principal surrogacy criteria. We consider a surrogate-dependent treatment efficacy curve that allows us to validate the surrogate at different time points. We extend these methods to accommodate a crossover design where all patients eventually receive the treatment. Because not all parameters are identified in the general setting, we rely on informative prior distributions to obtain inference. We consider the sensitivity of these prior assumptions as well as assumptions of independence among certain counterfactual quantities conditional on pre-treatment covariates to improve identifiability. We examine the frequentist properties of a Bayesian imputation method. Our work is motivated by a clinical trial of a gene therapy where the functional outcomes are measured repeatedly.

Authors who are presenting talks have a * after their name.

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