Statistical Inference with Mixed-Effect Model for Covariate-Adaptive Randomized Experiments
协变量自适应随机实验中混合效应模型的统计推断AI 解读 ↓
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协变量自适应随机实验中混合效应模型的统计推断
- 研究问题
- 当临床实验包含水平众多的因素并形成大量分层时，混合效应分析能否改善处理效应估计及推断的表现？
- 方法与思路
- 作者在协变量自适应随机实验中使用混合效应模型，将其与固定效应估计比较，并通过模拟和临床试验案例考察理论结果。
- 材料陈述的贡献
- 摘要称混合效应估计在所述情形下方差更低，这与随机化产生的边际不平衡有关；两类估计量随样本增长渐近等价。
- 阅读关注点
- 应核对随机化规则、混合效应设定、分层数与样本量的关系，以及方差比较和渐近等价结论分别需要哪些条件。
- 阅读边界
- 仅据版本未注明的索引摘要；随机效应设定、方差比较的完整条件及具体效应数值未提供，不能推广为所有实验设计下均更优。
原文摘要
Recent clinical studies increasingly involve a long factor with many levels, e.g., investigation sites, resulting in a large number of strata that must be accounted for either through design or subsequent analysis. This complication has prompted concerns from the U.S. Food and Drug Administration (FDA, 2023) regarding the adequacy of standard statistical methods, whose performance may deteriorate, and their properties become unclear when the number of strata is relatively large. In this work, we offer a first-time rigorous solution by employing mixed-effect models in covariate-adaptive randomized experiments. We show that the mixed-effect estimate achieves lower variance in treatment effect estimation than its fixed-effect counterpart in the presence of the long factor with many levels. This variance reduction is attributable to marginal imbalances induced by the randomization procedure, suggesting that designs promoting finer covariate balance lead to more efficient inference and increased statistical power. Furthermore, we demonstrate that, as the sample size grows, the mixed- and fixed-effect estimators become asymptotically equivalent. Our theoretical findings are validated through simulations and a clinical trial case study, and provide new insights on the design and analysis of clinical trials in the presence of a large number of strata.