A Non-Parametric Sensitivity Analysis for Bounding Bias in Hybrid Control Trials
混合对照试验中偏差界定的非参数敏感性分析AI 解读 ↓
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混合对照试验中偏差界定的非参数敏感性分析
- 研究问题
- 研究随机试验加入外部对照后，当同期与外部对照的条件均值可交换性被违反时，如何界定未测变量引起的处理效应偏差。
- 方法与思路
- 作者针对高效双重稳健估计量，用结果回归和试验参与的两个偏 R 平方参数描述最大偏差，并构造偏差调整后的置信界。
- 材料陈述的贡献
- 摘要称得到尖锐偏差界，并以等高线和稳健性值展示结论对未测混杂的敏感性；模拟和糖尿病试验应用展示该方法。
- 阅读关注点
- 应核对敏感性参数的定义、尖锐性和置信界有效性条件，以及参数校准方式、显著性推翻阈值与功效及样本量比较的设定。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未给出参数取值与数值结果，偏差界依赖所选敏感性强度而非消除混杂。
原文摘要
We study hybrid control trials (HCTs), in which a randomized controlled trial (RCT) is augmented with external control patients. Existing approaches for HCTs typically assume conditional exchangeability of the concurrent and external controls to identify trial-specific effects. When violated, this can induce substantial unquantified bias, which in turn limits the acceptability of HCTs in regulatory settings. We treat violations of mean exchangeability as omitted variable bias and develop a non-parametric sensitivity analysis that (i) applies to the efficient, doubly robust HCT estimator of the trial-specific ATE, and (ii) delivers sharp bounds on the bias induced by unmeasured covariates. Building on recent work in double machine learning, our approach characterizes the maximal bias in terms of two partial R-squared sensitivity parameters: the additional explanatory power that unmeasured confounders could have for the outcome regression and for trial participation. For any given choice of these parameters, we construct valid confidence bounds for bias-adjusted treatment effects and visualize critical causal gaps via contour plots and robustness values that show how strong unmeasured confounding would need to be to overturn nominally significant HCT findings. Through simulations, we show that the method (i) reliably upper-bounds true bias, (ii) restores type I error control in settings where naïve HCT analysis is anti-conservative, and (iii) can still deliver meaningful power gains and RCT sample-size reductions even under moderate violations of mean exchangeability. We illustrate the approach in a phase III trial on diabetes, supplemented with external controls. We discuss practical guidelines for designing and evaluating HCTs, including external-data selection, sample-size allocation, and interpretation of sensitivity contours.