Multiple Surrogates in the Meta-Analytic Setting for Normally Distributed Endpoints

The identification of good surrogate endpoints is a challenging endeavor. This may, at least partially, be attributable to the fact that most researchers have focused on the identification of a single surrogate endpoint. It is thus implicitly assumed that the treatment effect on the true endpoint (<i>T</i>) can be accurately predicted based on the treatment effect on <i>one</i> surrogate endpoint (<i>S</i>) only. Given the complex nature of many diseases and the different therapeutic pathways in which a treatment can impact <i>T</i>, this assumption may be too optimistic. For example, in oncology, the effect of a treatment often depends on <i>both</i> the treatment’s efficacy and its toxicity. In the present article, the meta-analytic framework of? is extended to the setting where multiple <i>S</i> are considered. To cope with potential model convergence issues that often arise in a meta-analytic framework, several simplified model fitting strategies are proposed. Further, simulation studies are conducted to evaluate the properties of the estimated surrogacy metrics, and the new methodology is applied on a case study in schizophrenia. An online Appendix that details how the analyses can be conducted in practice (using the R package <i>Surrogate</i>) is also provided.