  Conventional methods for analyzing composite endpoints in clinical trials
often only focus on the time to the first occurrence of all events in the
composite. Therefore, they have inherent limitations because the individual
patients' first event can be the outcome of lesser clinical importance. To
overcome this limitation, the concept of the win ratio (WR), which accounts for
the relative priorities of the components and gives appropriate priority to the
more clinically important event, was examined. For example, because mortality
has a higher priority than hospitalization, it is reasonable to give a higher
priority when obtaining the WR. In this paper, we evaluate three innovative WR
methods (stratified matched, stratified unmatched, and unstratified unmatched)
for two and multiple components under binary and survival composite endpoints.
We compare these methods to traditional ones, including the Cox regression,
O'Brien's rank-sum-type test, and the contingency table for controlling study
Type I error rate. We also incorporate these approaches into two-stage
enrichment designs with the possibility of sample size adaptations to gain
efficiency for rare disease studies.
