Efficient Estimation of Causal Effects Under Two-Phase Sampling with Error-Prone Outcome and Treatment Measurements
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两阶段抽样下结局与处理均有测量误差的高效因果效应估计
- 研究问题
- 电子健康记录中处理和结局均可能误测，且验证样本依赖初始误测信息抽取时，如何估计平均处理效应并改善有限样本稳定性？
- 方法与思路
- 作者构造两个渐近等价的双重稳健估计量，联系两种一般两阶段高效估计思路，分析不稳定来源并提出有限样本改进。
- 材料陈述的贡献
- 摘要称该研究统一此前分离的估计思路，改进可用于更一般的两阶段抽样场景，并以模拟和抗逆转录病毒治疗应用展示方法。
- 阅读关注点
- 核对第二阶段抽样概率与验证机制要求、两种估计量等价的条件，以及稳定化修改是否影响双重稳健性和效率。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给出识别条件、修改公式或模拟数值，不能判断稳定性改进的具体幅度。
原文摘要
Measurement error is a common challenge for causal inference studies using electronic health record (EHR) data, where clinical outcomes and treatments are frequently mismeasured. Researchers often address measurement error by conducting manual chart reviews to validate measurements in a subset of the full EHR data -- a form of two-phase sampling. To improve efficiency, phase-two samples are often collected in a biased manner dependent on the patients' initial, error-prone measurements. In this work, motivated by our aim of performing causal inference with error-prone outcome and treatment measurements under two-phase sampling, we develop solutions applicable to both this specific problem and the broader problem of causal inference with two-phase samples. For our specific measurement error problem, we construct two asymptotically equivalent doubly-robust estimators of the average treatment effect and demonstrate how these estimators arise from two previously disconnected approaches to constructing efficient estimators in general two-phase sampling settings. We document various sources of instability affecting estimators from each approach and propose modifications that can considerably improve finite sample performance in any two-phase sampling context. We demonstrate the utility of our proposed methods through simulation studies and an illustrative example assessing effects of antiretroviral therapy on occurrence of AIDS-defining events in patients with HIV from the Vanderbilt Comprehensive Care Clinic.