Bayesian evaluation of surrogate endpoints.

Abstract

To save time and reduce the size and cost of clinical trials, surrogate endpoints are frequently measured instead of true endpoints. The proportion of the treatment effect explained by surrogate endpoints (PTE) is a widely used, albeit controversial, validation criteria. Frequentist and Bayesian methods have been developed to facilitate such validation. The former does not formally incorporate prior information; a critical issue since confidence intervals on PTE is often unacceptably wide. Both the Bayesian and frequentist approaches may yield estimates of PTE outside the unit interval. Furthermore, the existing Bayesian method offers no insight into the prior used for PTE, making prior-to-posterior sensitivity analyses problematic. We proposed a fully Bayesian approach that avoids both of these problems. We also consider the effect of interaction on inference for PTE. As an alternative to the use of PTE, we develop a Bayesian model for relative effect and the association between surrogate and true endpoints, making use of power priors.