Validation of Surrogate Endpoints in Multiple Randomized Clinical Trials with Discrete Outcomes

Abstract

AbstractThis article extends the work of Buyse et al. (2000) on the validation of surrogate endpoints in a meta‐analytic setting to the case of two discrete outcomes, the focus being on binary endpoints. The methodology entails fitting of a joint model for the surrogate and the true endpoints that includes several random effects. We propose to fit this model using a pairwise likelihood (PL) approach which seems better suited to the problem at hand than maximum likelihood or penalized quasi‐likelihood. The performance of the PL estimator is evaluated on the grounds of limited simulations and the methodology is illustrated on data from a meta‐analysis of five clinical trials comparing antipsychotic agents for the treatment of chronic schizophrenia.