We revisit the well-known but often misunderstood issue of (non)collapsibility of effect measures in regression models for binary and time-to-event outcomes. We describe an existing simple but largely ignored procedure for marginalizing estimates of conditional odds ratios and propose a similar procedure for marginalizing estimates of conditional hazard ratios (allowing for right censoring), demonstrating its performance in simulation studies and in a reanalysis of data from a small randomized trial in primary biliary cirrhosis patients. In addition, we aim to provide an educational summary of issues surrounding (non)collapsibility from a causal inference perspective and to promote the idea that the words conditional and adjusted (likewise marginal and unadjusted) should not be used interchangeably.

中文翻译：

---

从橙子变成苹果：比较不可折叠效应估计量及其在调整不同协变量集后的标准误差


我们重新审视二元和事件时间结果的回归模型中效果测量的（不可）折叠性这一众所周知但经常被误解的问题。我们描述了一种现有的简单但很大程度上被忽视的程序，用于边缘化条件比值比的估计，并提出了一个类似的程序，用于边缘化条件风险比的估计（允许正确的审查），证明了其在模拟研究和对小规模数据的重新分析中的性能。原发性胆汁性肝硬化患者的随机试验。此外，我们的目标是从因果推理的角度提供有关（非）可折叠性问题的教育总结，并宣扬“有条件”和“调整”（同样是“边际”和“未调整”）这两个词不应互换使用的想法。