Benz Luke, Mukherjee Rajarshi, Wang Rui, Arterburn David, Fischer Heidi, Lee Catherine, Shortreed Susan M, Haneuse Sebastien
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Am J Epidemiol. 2024 Dec 26. doi: 10.1093/aje/kwae471.
Target trial emulation (TTE) is a popular framework for observational studies based on electronic health records (EHR). A key component of this framework is determining the patient population eligible for inclusion in both a target trial of interest and its observational emulation. Missingness in variables that define eligibility criteria, however, presents a major challenge towards determining the eligible population when emulating a target trial with an observational study. In practice, patients with incomplete data are almost always excluded from analysis despite the possibility of selection bias, which can arise when subjects with observed eligibility data are fundamentally different than excluded subjects. Despite this, to the best of our knowledge, very little work has been done to mitigate this concern. In this paper, we propose a novel conceptual framework to address selection bias in TTE studies, tailored towards time-to-event endpoints, and describe estimation and inferential procedures via inverse probability weighting (IPW). Under an EHR-based simulation infrastructure, developed to reflect the complexity of EHR data, we characterize common settings under which missing eligibility data poses the threat of selection bias and investigate the ability of the proposed methods to address it. Finally, using EHR databases from Kaiser Permanente, we demonstrate the use of our method to evaluate the effect of bariatric surgery on microvascular outcomes among a cohort of severely obese patients with Type II diabetes mellitus (T2DM).
目标试验模拟（TTE）是一种基于电子健康记录（EHR）的观察性研究常用框架。该框架的一个关键组成部分是确定既符合感兴趣的目标试验又符合其观察性模拟纳入标准的患者群体。然而，定义纳入标准的变量中存在缺失值，这在通过观察性研究模拟目标试验时，给确定符合条件的人群带来了重大挑战。在实际操作中，尽管存在选择偏倚的可能性，但几乎总是将数据不完整的患者排除在分析之外，当有观察到的纳入标准数据的受试者与被排除的受试者存在根本差异时，就可能产生选择偏倚。尽管如此，据我们所知，为减轻这一问题所做的工作非常少。在本文中，我们提出了一个新颖的概念框架，以解决TTE研究中的选择偏倚问题，该框架针对事件发生时间终点进行了定制，并通过逆概率加权（IPW）描述了估计和推断程序。在一个基于EHR的模拟基础设施下，该基础设施旨在反映EHR数据的复杂性，我们描述了缺失纳入标准数据构成选择偏倚威胁的常见情况，并研究了所提出方法解决该问题的能力。最后，我们使用凯撒医疗集团的EHR数据库，展示了我们的方法在评估减肥手术对一组重度肥胖的II型糖尿病（T2DM）患者微血管结局影响方面的应用。
