  For randomized clinical trials where a single, primary, binary endpoint would
require unfeasibly large sample sizes, composite endpoints are widely chosen as
the primary endpoint. Despite being commonly used, composite endpoints entail
challenges in designing and interpreting results. Given that the components may
be of different relevance and have different effect sizes, the choice of
components must be made carefully. Especially, sample size calculations for
composite binary endpoints depend not only on the anticipated effect sizes and
event probabilities of the composite components, but also on the correlation
between them. However, information on the correlation between endpoints is
usually not reported in the literature which can be an obstacle for planning of
future sound trial design. We consider two-arm randomized controlled trials
with a primary composite binary endpoint and an endpoint that consists only of
the clinically more important component of the composite endpoint. We propose a
trial design that allows an adaptive modification of the primary endpoint based
on blinded information obtained at an interim analysis. We consider a decision
rule to select between a composite endpoint and its most relevant component as
primary endpoint. The decision rule chooses the endpoint with the lower
estimated required sample size. Additionally, the sample size is reassessed
using the estimated event probabilities and correlation, and the expected
effect sizes of the composite components. We investigate the statistical power
and significance level under the proposed design through simulations. We show
that the adaptive design is equally or more powerful than designs without
adaptive modification on the primary endpoint. The targeted power is achieved
even if the correlation is misspecified while maintaining the type 1 error. We
illustrated the proposal by means of two case studies.
