基于电子健康档案的比较效果研究中针对信息性缺失数据的双重抽样
Double sampling for informatively missing data in electronic health record-based comparative effectiveness research
AI总结：
本文针对电子健康档案中结局数据非随机缺失问题，提出双重抽样设计以支持因果效应估计与推断，并推导出非参数模型和MAR模型下的高效稳健估计量，同时将方法扩展至任意粗化机制。
摘要：
缺失数据在大多数应用场景中都会出现，并且在电子健康档案（EHR）中无处不在。当数据相对于已测量的协变量而言并非随机缺失（MNAR）时，通常会考虑进行敏感性分析。然而，这些事后解决方案往往不能令人满意，因为它们无法保证得出具体的结论。受一项基于EHR的减重手术后长期结局研究的启发，当统计目标是关于因果效应的估计与推断时，我们考虑使用双重抽样作为缓解MNAR结局数据问题的一种手段。我们描述了在该设计下足以识别混杂因素、治疗和结局联合分布的假设。此外，我们在非参数模型下以及在假设结局实际上最初是随机缺失（MAR）的模型下，推导了平均因果治疗效应的高效且稳健的估计量。我们在模拟中将这些估计量与一种根据违反MAR假设的证据进行自适应估计的方法进行了比较。最后，我们还表明，所提出的双重抽样设计可以扩展用于处理任意的粗化机制，并推导出任意光滑全数据泛函的非参数高效估计量。
英文摘要：
Missing data arise in most applied settings and are ubiquitous in electronic health records (EHR). When data are missing not at random (MNAR) with respect to measured covariates, sensitivity analyses are often considered. These post-hoc solutions, however, are often unsatisfying in that they are not guaranteed to yield concrete conclusions. Motivated by an EHR-based study of long-term outcomes following bariatric surgery, we consider the use of double sampling as a means to mitigate MNAR outcome data when the statistical goals are estimation and inference regarding causal effects. We describe assumptions that are sufficient for the identification of the joint distribution of confounders, treatment, and outcome under this design. Additionally, we derive efficient and robust estimators of the average causal treatment effect under a nonparametric model and under a model assuming outcomes were, in fact, initially missing at random (MAR). We compare these in simulations to an approach that adaptively estimates based on evidence of violation of the MAR assumption. Finally, we also show that the proposed double sampling design can be extended to handle arbitrary coarsening mechanisms, and derive nonparametric efficient estimators of any smooth full data functional.
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