Optimizing Primary Analyses in Randomized Controlled Trials with Multiple Endpoints: A Simulation Study with Application to Kidney Transplantation

Relying on a single primary endpoint in randomized controlled trials (RCTs) is often not feasible, e.g. due to low event rates. We explored three approaches to consider multiple endpoints in the primary analysis of RCTs, as stated in the FDA and EMA guidelines on multiplicity issues: (i) a composite endpoint (CE), (ii) multiple testing and multiplicity correction (MTMC), and (iii) a hierarchical non-parametric procedure, called generalized pairwise comparisons (GPC). By means of clinical trial simulations, a broad range of scenarios were investigated to assess the operating characteristics of different strategies to incorporate multiple endpoints into the primary analysis of two-arm RCTs. Both time-to-event and binary endpoints were explored. When testing time-to-event endpoints, we found that the composite approach and GPC were outperforming MTMC in the majority of scenarios. Furthermore, we observed that testing a binary compared to a time-to-event composite endpoint only marginally decreased power. As trials using multiple endpoints are on the rise, understanding how the approaches make use of the limited information available in the analysis of commonly used endpoints will be crucial. Our work supports the choice of endpoint definitions and associated analysis approaches in future trials.