Incorporating Data from Multiple Endpoints in the Analysis of Clinical Trials: Example from RSV Vaccines

Background:To meet regulatory approval, interventions must demonstrate efficacy against a primary outcome in randomized clinical trials. However, when there are multiple clinically relevant outcomes, selecting a single primary outcome is challenging. Incorporating data from multiple outcomes may increase statistical power in clinical trials. We examined methods for analyzing data on multiple endpoints, inspired by real-world trials of interventions against respiratory syncytial virus (RSV).Method:We developed a novel permutation test representing a weighted average of individual outcome test statistics (wavP) to evaluate intervention efficacy in a multiple endpoint analysis. We compared the power and type I error rate of this approach to the Bonferroni correction (bonfT) and the minP permutation test. We evaluated the different approaches using simulated data from three hypothetical trials varying the intervention efficacy, correlation, and incidence of the outcomes, and data from a real-world RSV clinical trial.Results:When the vaccine efficacy against different outcomes was similar, wavP yielded higher power than bonfT and minP; in some scenarios the improvement in power was substantial. In settings where vaccine efficacy was notably larger against one endpoint compared with the others, all three methods had similar power. We developed an R package, PERmutation basEd ANalysis of mulTiple Endpoints (PERMEATE), to guide the selection of the most appropriate method for analyzing multiple endpoints in clinical trials.Conclusions:Analyzing multiple endpoints using a weighted permutation method can increase power, whereas controlling the type I error rate compared with established methods under conditions mirroring real-world RSV clinical trials.