发表机构
University of Tsukuba; Astellas Pharma Global Development, Inc.; Wakayama Medical University; University of Osaka（筑波大学; 安斯泰来全球研发有限公司; 和歌山医科大学; 大阪大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对偏态纵向临床数据，提出Box-Cox多元回归框架，允许变换参数随组和时间变化，支持中位数差异和概率型处理效应推断，实现无偏且稳健的估计。
AI 中文摘要
临床试验中的纵向连续结局通常使用混合效应模型重复测量（MMRM）在正态性假设下进行分析。然而，许多临床结局呈偏态分布，使得基于均值的处理效应难以解释，并可能降低统计效率。Box-Cox MMRM（BCMMRM）方法通过逆变换实现对基于模型的中位数差异的推断，从而适应偏态性。然而，BCMMRM通常假设处理组和时间点之间具有共同的变换参数。当分布形状在组间不同或随时间演变时，该假设可能导致有偏的处理效应。此外，当处理不仅影响集中趋势，还影响分布形状或尾部行为时，处理效应可能无法通过单一位置汇总（如中位数）充分表征。我们提出了Box-Cox多元回归（BCMVR）框架，用于具有偏态结局的纵向数据。BCMVR通过允许变换参数在不同组和时间点之间变化来放宽这一限制。该框架支持基于可解释汇总的推断，包括中位数差异和基于概率的处理效应，该效应量化了一组中随机选择的患者比另一组患者结局更好的概率。该度量整合了整个结局分布的信息，并在分布形状不同时提供补充性汇总。模拟研究表明，当分布形状不同时，BCMMRM可能产生有偏估计，而BCMVR提供近乎无偏的估计。基于概率的度量在稳健性和统计效率之间取得了有利的平衡。所提出的框架为分布异质性下的处理效应推断提供了一种灵活且可解释的方法。
英文摘要
Longitudinal continuous outcomes in clinical trials are commonly analyzed using mixed models for repeated measures (MMRM) under normality assumptions. However, many clinical outcomes are skewed, making mean-based treatment effects difficult to interpret and potentially reducing statistical efficiency. The Box--Cox MMRM (BCMMRM) approach accommodates skewness by enabling inference on model-based median differences via inverse transformation. However, BCMMRM typically assumes a common transformation parameter across treatment groups and time points. When distributional shapes differ between groups or evolve over time, this assumption may lead to biased treatment effect. Furthermore, when treatment affects not only central tendency but also distributional shape or tail behavior, treatment effects may not be adequately characterized by a single location summary such as the median. We propose the Box--Cox multivariate regression (BCMVR) framework for longitudinal data with skewed outcomes. BCMVR relaxes this restriction by allowing transformation parameters to vary across groups and time points. The framework enables inference based on interpretable summaries, including median differences and a probability-based treatment effect quantifying the probability that a randomly selected patient in one group has a better outcome than one in another group. This measure integrates information over the entire outcome distribution and provides a complementary summary when distributional shapes differ. Simulation studies demonstrate that BCMMRM can produce biased estimates when distributions differ in shape, whereas BCMVR provides nearly unbiased estimation. The probability-based measure achieves a favorable balance between robustness and statistical efficiency. The proposed framework provides a flexible and interpretable approach to treatment effect inference under distributional heterogeneity.
