AI 中文总结
本文针对临床试验纵向数据分析中MMRM模型的收敛与效率问题，通过模拟及糖尿病数据应用，给出不同样本量下MMRM的实践指导，优化了模型的收敛率与覆盖率。
AI 中文摘要
重复测量混合模型（MMRM）是临床试验中分析纵向数据的常用方法。然而，实际挑战如小样本量、大量时间点、以及受试者内误差的方差-协方差结构选择，常阻碍模型收敛与有效推断。本文通过大量模拟研究及糖尿病试验数据的应用，评估不同场景下使用MMRM的不同选项。研究表明，采用异方差自回归协方差结构的经验偏差校正系数协方差调整，在中、大样本设计中可实现接近标称的覆盖率与高收敛率；对于小样本量，包含基线协变量及处理-时间点交互项的简单模型可达到良好效率与高收敛概率。基于上述结果，本文为从业者提供将MMRM应用于临床试验数据的可操作指导。
英文摘要
Mixed models for repeated measures (MMRM) are a popular method for analyzing longitudinal data in clinical trials. However, practical challenges, such as small sample sizes, large numbers of time points, and selection of variance-covariance structure for within-subject errors, often present barriers to model convergence and valid inference. This article evaluates different options for using MMRM in different scenarios through extensive simulation studies and an application on diabetes trial data. We demonstrate that the empirical bias-reduced coefficient covariance adjustment with the heterogeneous autoregressive covariance structure yields near-nominal coverage with high convergence rates for moderate and large sample designs. For small sample sizes, the simple model with baseline covariates and treatment by time point interaction achieves good efficiency and high probability of convergence. Based on these results, we provide practitioners with actionable guidance for applying MMRM to clinical trial data.
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