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.

翻译：由于提出了综合终点、净效益(NB)和赢率(WW)等时间对时间对活动首先分析的替代办法,因此提出了综合终点、净效益(NB)和赢率比率(WR)的替代方法,利用临床重要性的优先成分结果评估治疗效果;然而,NB和WR的统计推论依赖大量抽样假设,这可能导致无效的测试统计和不充分的、不令人满意的信任间隔,特别是当抽样规模小或赢率比例接近0或1时,我们制定了一种系统办法,在配对抽样设计中解决这些限制。我们首先在无治疗差异的空假设下推出新的试验统计。然后,我们提出计算抽样规模的公式。最后,我们为这两个估计者制定信心间隔估计。为了估计信任间隔,我们使用千差法来将两个单独的个人比例间间间间隔结合到一个混合间隔(我们评估了拟议的测试统计和OM的接近信任期的绩效,在进行模拟时,我们用宽度的中间比例来计算。