Validation of surrogate endpoints in multiple randomized clinical trials with discrete outcomes

This 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.