The primary analysis for longitudinal randomized controlled trials (RCTs) often compares treatment groups at the last timepoint, referred to as the landmark time. Assuming data are normally distributed and missing at random, the mixed model for repeated measures (MMRM) is widely used to conduct inference in terms of a mean difference. When outcomes violate normality assumption and/or the mean difference lacks a clear interpretation, we may quantify treatment effects using the probability that a treated participant would have a better outcome than (or win over) a control participant. For RCTs with missing data, one may apply the generalized pairwise comparison (GPC) procedure, which carries forward the results of a pairwise comparison from a previous timepoint. We propose first using ranks to converts each observation at a timepoint into a win fraction, reflecting the proportion of times that the observation is better than every observation in the comparison group. Then, we conduct inference for the win probability based on the win fractions using the MMRM to obtain the point and variance estimates. Simulation results suggest that our method performed much better than the GPC procedure. We illustrate our proposed procedure in SAS and R using data from two published trials.

翻译：纵向随机对照试验（RCT）的主要分析通常比较治疗组在最终时间点（称为界标时间）的情况。假设数据服从正态分布且为随机缺失，重复测量混合模型（MMRM）被广泛用于基于均值差异进行统计推断。当结局变量违反正态性假设和/或均值差异缺乏明确解释时，我们可以通过计算治疗组参与者结局优于（或"战胜"）对照组参与者的概率来量化治疗效果。对于存在缺失数据的RCT，可采用广义成对比较（GPC）方法，该方法将先前时间点的成对比较结果向前推移。我们首先提出使用秩将每个时间点的观测值转换为胜率分数，该分数反映该观测值优于比较组中所有观测值的比例。随后，我们基于这些胜率分数，利用MMRM进行胜率推断，以获得点估计和方差估计。模拟结果表明，我们的方法性能显著优于GPC方法。我们使用两个已发表试验的数据，在SAS和R中演示了所提出的方法。