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Title: Flexible evaluation of surrogacy in Bayesian adaptive platform studies Authors:  Michael Sachs - University of Copenhagen (Denmark) [presenting]
Erin Gabriel - University of Copenhagen (Denmark)
Alessio Crippa - Karolinska Institute (Sweden)
Michael Daniels - University of Florida (United States)
Abstract: Trial-level surrogates are useful tools for improving the speed and cost-effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. As Bayesian adaptive platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. They also offer a set of statistical issues, including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner. We demonstrate how our method can be used in a simulated illustrative example based on an ongoing platform study in prostate cancer.