Rank-based methods for estimating landmark win probability in longitudinal randomized controlled trials with missing data
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缺失数据下纵向随机试验终点胜出概率的秩方法
- 研究问题
- 纵向随机试验存在缺失数据，且均值差不易解释或结局不满足正态假设时，如何估计最终时点治疗组优于对照组的概率？
- 方法与思路
- 作者先将各时点观测按秩转化为相对于另一组的胜出比例，再用重复测量混合模型获得最终时点胜出概率及方差估计。
- 材料陈述的贡献
- 摘要称该方法在模拟中优于沿用先前时点比较结果的广义成对比较程序，并用两项已发表试验展示 SAS 与 R 实现。
- 阅读关注点
- 应核对缺失机制假设、并列结局的处理、秩转换后的相关结构，以及混合模型方差估计是否反映跨组比较带来的依赖。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未明确新方法的缺失与分布条件，也未给出性能差异的具体数值。
原文摘要
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.