Implementing the meta-analytic approach for the evaluation of surrogate endpoints in SAS and R: a word of caution.

The meta-analytic approach has become the gold-standard methodology for the evaluation of surrogate endpoints and several implementations are currently available in SAS and R. The methodology is based on hierarchical models that are numerically demanding and, when the amount of data is limited, maximum likelihood algorithms may not converge or may converge to an ill-conditioned maximum such as a boundary solution. This may produce misleading conclusions and have negative implications for the evaluation of new drugs. In the present work, we explore the use of two distinct functions in R (<i>lme</i> and <i>lmer</i>) and the <i>MIXED</i> procedure in SAS to assess the validity of putative surrogate endpoints in the meta-analytic framework, via simulations and the analysis of a real case study. We describe some problems found with the <i>lmer</i> function in R that led to a poorer performance as compared with the <i>lme</i> function and <i>MIXED</i> procedure.