Background: To achieve licensure, interventions typically must demonstrate efficacy against a primary outcome in a randomized clinical trial. However, selecting a single primary outcome a priori is challenging. Incorporating data from multiple and related outcomes might help to increase statistical power in clinical trials. Inspired by real-world clinical trials of interventions against respiratory syncytial virus (RSV), we examined methods for analyzing data on multiple endpoints.
Method: We simulated data from three different populations in which the efficacy of the intervention and the correlation among outcomes varied. We developed a novel permutation-based approach that represents a weighted average of individual outcome test statistics ( varP ) to evaluate intervention efficacy in a multiple endpoint analysis. We compared the power and type I error rate of this approach to two alternative methods: the Bonferroni correction ( bonfT ) and another permutation-based approach that uses the minimum P-value across all test statistics ( minP ).
Results: When the vaccine efficacy against different outcomes was similar, VarP 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 to the others, all three methods had similar power.
Conclusions: Analyzing multiple endpoints using a weighted permutation method can increase power while controlling the type I error rate in settings where outcomes share similar characteristics, like RSV outcomes. We developed an R package, PERMEATE , to guide selection of the most appropriate method for analyzing multiple endpoints in clinical trials.