Robust integration of external control data in randomized trials
随机试验中外部对照数据的稳健整合AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以随机试验人群中的处理效应为目标，讨论外部对照可交换性、加权增广估计与稳健整合，属于因果推断。
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随机试验中外部对照数据的稳健整合
- 研究问题
- 如何利用外部对照提高随机试验处理效应估计效率，同时在试验与外部人群不具可交换性时仍保持有效推断？
- 方法与思路
- 作者刻画利用随机化信息的估计量类，并将高效的仅试验估计量与优化的随机化感知估计量组合，进行模拟和试验应用。
- 材料陈述的贡献
- 摘要称，组合估计量具有一致性，且无论可交换性是否成立，其效率都不低于两个组成估计量中更高效者。
- 阅读关注点
- 应核对外部对照进入估计的方式、组合权重构造，以及一致性、渐近正态性和效率比较所依赖的条件。
- 阅读边界
- 这是预印本，同行评审或期刊接收未核实；摘要未列出完整正则条件，效率结论不能直接解释为所有有限样本都更优。
原文摘要
One approach for increasing the efficiency of randomized trials is the use of "external controls" -- individuals who received the control treatment studied in the trial during routine practice or in prior experimental studies. Existing external control methods, however, can be biased if the populations underlying the trial and the external control data are not exchangeable. Here, we characterize a randomization-aware class of treatment effect estimators in the population underlying the trial that remain consistent and asymptotically normal when using external control data, even when exchangeability does not hold. We consider two members of this class of estimators: the well-known augmented inverse probability weighting trial-only estimator, which is the efficient estimator when only trial data are used; and a potentially more efficient member of the class when exchangeability holds and external control data are available, which we refer to as the optimized randomization-aware estimator. To achieve robust integration of external control data in trial analyses, we then propose a combined estimator based on the efficient trial-only estimator and the optimized randomization-aware estimator. We show that the combined estimator is consistent and no less efficient than the most efficient of the two component estimators, whether the exchangeability assumption holds or not. We examine the estimators' performance in simulations and we illustrate their use with data from two trials of paliperidone extended-release for schizophrenia.