Non-collapsibility and Built-in Selection Bias of Hazard Ratio in Randomized Controlled Trials
随机对照试验中风险比的不可折叠性与内生选择偏差AI 解读 ↓
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随机对照试验中风险比的不可折叠性与内生选择偏差
- 研究问题
- 随机对照试验中，条件风险比与边际风险比的差异，如何区分为不可折叠性和内生选择偏差，分时段风险比如何解释？
- 方法与思路
- 作者通过模拟区分两类效应，考察逆概率治疗加权 Cox 模型估计边际风险比的表现，并分析分时段风险比。
- 材料陈述的贡献
- 摘要称，加权 Cox 估计在所述设定中无偏，内生选择偏差体现于分时段风险比，并据此建议避免使用后者。
- 阅读关注点
- 应核对边际风险比的定义、模拟中的比例风险设定、加权模型条件，以及分时段选择机制和整体风险比的关系。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列明无偏结论的完整条件，避免分时段风险比的建议仍需核对。
原文摘要
Background: The hazard ratio of the Cox proportional hazards model is widely used in randomized controlled trials to assess treatment effects. However, two properties of the hazard ratio including the non-collapsibility and built-in selection bias need to be further investigated. Methods: We conduct simulations to differentiate the non-collapsibility effect and built-in selection bias from the difference between the marginal and the conditional hazard ratio. Meanwhile, we explore the performance of the Cox model with inverse probability of treatment weighting for covariate adjustment when estimating the marginal hazard ratio. The built-in selection bias is further assessed in the period-specific hazard ratio. Results: The conditional hazard ratio is a biased estimate of the marginal effect due to the non-collapsibility property. In contrast, the hazard ratio estimated from the inverse probability of treatment weighting Cox model provides an unbiased estimate of the true marginal hazard ratio. The built-in selection bias only manifests in the period-specific hazard ratios even when the proportional hazards assumption is satisfied. The Cox model with inverse probability of treatment weighting can be used to account for confounding bias and provide an unbiased effect under the randomized controlled trials setting when the parameter of interest is the marginal effect. Conclusions: We propose that the period-specific hazard ratios should always be avoided due to the profound effects of built-in selection bias.