  As alternatives to the time-to-first-event analysis of composite endpoints,
the {\it net benefit} (NB) and the {\it win ratio} (WR) -- which assess
treatment effects using prioritized component outcomes based on clinical
importance -- have been proposed. However, statistical inference of NB and WR
relies on a large-sample assumptions, which can lead to an invalid test
statistic and inadequate, unsatisfactory confidence intervals, especially when
the sample size is small or the proportion of wins is near 0 or 1.
  In this paper, we develop a systematic approach to address these limitations
in a paired-sample design. We first introduce a new test statistic under the
null hypothesis of no treatment difference. Then, we present the formula to
calculate the sample size. Finally, we develop the confidence interval
estimations of these two estimators. To estimate the confidence intervals, we
use the {\it method of variance estimates recovery} (MOVER), that combines two
separate individual-proportion confidence intervals into a hybrid interval for
the estimand of interest. We assess the performance of the proposed test
statistic and MOVER confidence interval estimations through simulation studies.
  We demonstrate that the MOVER confidence intervals are as good as the
large-sample confidence intervals when the sample is large and when the
proportions of wins is bounded away from 0 and 1. Moreover, the MOVER intervals
outperform their competitors when the sample is small or the proportions are at
or near the boundaries 0 and 1. We illustrate the method (and its competitors)
using three examples from randomized clinical studies.
