Built-in Selection Bias in Proportional Hazards Models with Omitted Covariates: Simulation Evidence and Alternative Approaches
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遗漏协变量的比例风险模型内生选择偏差：模拟证据与替代方法
- 研究问题
- 即使处理在基线随机分配，遗漏影响生存的异质性为何仍会改变比例风险模型的风险比解释，不同替代指标能提供什么信息？
- 方法与思路
- 作者通过模拟比较 Cox 与参数比例风险模型，并考察脆弱性模型、加速失效时间模型、生存差异和时间变化处理效应模型。
- 材料陈述的贡献
- 摘要称，文章梳理遗漏异质性引发风险比偏差的文献与模拟证据，并通过放射治疗随机试验说明替代分析方法的实际意义。
- 阅读关注点
- 核对各方法对应的条件或边际目标，区分风险比非可折叠性与估计偏差，并检查遗漏异质性、生存选择及时间变化的设定。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出模拟参数与偏差幅度，也不能据此认定所有替代方法均稳健。
原文摘要
In time-to-event analysis, the hazard ratio (HR) derived from the Cox proportional hazards (PH) model is the most commonly used and widely reported measure for assessing treatment effects. However, hazard ratios are non-collapsible due to their inherent conditioning on survival up to each time point. As a result, they are subject to built-in selection bias in the presence of unmeasured heterogeneity arising from omitted important covariates, even when these covariates are independent of the main exposure at baseline, as is the case in randomized controlled trials. This article aims to provide an overview of key findings from the literature on how unobserved heterogeneity, due to omitted covariates that affect the outcome, can bias the estimation of the treatment hazard ratio in standard proportional hazards models, even in randomized trials where treatment is assigned independently of such covariates. Through simulations, we evaluate the extent of bias in the semi-parametric Cox PH model and parametric PH model under various scenarios of unmeasured heterogeneity. We then compare these standard models to alternative approaches that either account for this issue or are considered robust to it. These alternatives include the hazard ratio estimated from frailty models, regression parameters from an Accelerated Failure Time (AFT) model, and survival differences between treatment groups estimated nonparametrically using Kaplan-Meier curves or based on a Cox model with time-dependent effect of the exposure. We illustrate the practical relevance of the explored alternatives through a real data application to a randomized controlled trial from the Radiation Therapy Oncology Group (RTOG 9202).