Clarifying the Role of the Mantel-Haenszel Risk Difference Estimator in Randomized Clinical Trials
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厘清随机临床试验中 Mantel–Haenszel 风险差估计量的作用
- 研究问题
- 在随机临床试验中，层间风险差不恒定时，Mantel–Haenszel 估计量能否估计平均处理效应，并获得跨分层渐近情形有效的方差估计？
- 方法与思路
- 作者将该估计量视为协变量调整方法，在大层、稀疏层和混合渐近情形下分析其性质，并提出统一稳健方差估计量。
- 材料陈述的贡献
- 摘要称在风险差变异的合理限制下可一致估计平均处理效应，并给出不依赖共同风险差假设、跨渐近情形一致的方差估计。
- 阅读关注点
- 应核对风险差变异限制、层权重与目标人群平均效应的关系，以及不同渐近情形下方差公式和既有方法比较的依据。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未明确变异限制和改进幅度，不能认定任意异质风险差下都估计平均处理效应。
原文摘要
The Mantel-Haenszel (MH) risk difference estimator, commonly used in randomized clinical trials for binary outcomes, calculates a weighted average of stratum-specific risk difference estimators. Traditionally, this method requires the stringent assumption that risk differences are homogeneous across strata, also known as the common (constant) risk difference assumption. In our article, we relax this assumption and adopt a modern perspective, viewing the MH risk difference estimator as an approach for covariate adjustment in randomized clinical trials, distinguishing its use from that in meta-analysis and observational studies. We demonstrate that, under reasonable restrictions on risk difference variability, the MH risk difference estimator consistently estimates the average treatment effect within a standard super-population framework, which is often the primary interest in randomized clinical trials, in addition to estimating a weighted average of stratum-specific risk differences. We rigorously study its properties under the large-stratum and sparse-stratum asymptotic regimes, as well as under mixed-regime settings. Furthermore, for either estimand, we propose a unified robust variance estimator that improves over the popular variance estimators by Greenland and Robins (1985) and Sato et al. (1989) and has provable consistency across these asymptotic regimes, regardless of assuming common risk differences. Extensions of our theoretical results also provide new insights into the Mantel-Haenszel test, the post-stratification estimator, and settings with multiple treatments. Our findings are thoroughly validated through simulations and a clinical trial example.