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For randomized clinical trials where a single, primary, binary endpoint wouldrequire unfeasibly large sample sizes, composite endpoints are widely chosen asthe primary endpoint. Despite being commonly used, composite endpoints entailchallenges in designing and interpreting results. Given that the components maybe of different relevance and have different effect sizes, the choice ofcomponents must be made carefully. Especially, sample size calculations forcomposite binary endpoints depend not only on the anticipated effect sizes andevent probabilities of the composite components, but also on the correlationbetween them. However, information on the correlation between endpoints isusually not reported in the literature which can be an obstacle for planning offuture sound trial design. We consider two-arm randomized controlled trialswith a primary composite binary endpoint and an endpoint that consists only ofthe clinically more important component of the composite endpoint. We propose atrial design that allows an adaptive modification of the primary endpoint basedon blinded information obtained at an interim analysis. We consider a decisionrule to select between a composite endpoint and its most relevant component asprimary endpoint. The decision rule chooses the endpoint with the lowerestimated required sample size. Additionally, the sample size is reassessedusing the estimated event probabilities and correlation, and the expectedeffect sizes of the composite components. We investigate the statistical powerand significance level under the proposed design through simulations. We showthat the adaptive design is equally or more powerful than designs withoutadaptive modification on the primary endpoint. The targeted power is achievedeven if the correlation is misspecified while maintaining the type 1 error. Weillustrated the proposal by means of two case studies.

Adaptive clinical trial designs with blinded selection of binary composite endpoints and sample size reassessment