Robust methods for missing data in electronic health records-based studies

项目总结 电子健康记录(EHR)数据为具有成本效益的临床和公共卫生带来了巨大的机遇 研究，尤其是当随机试验或前瞻性观察性研究不可行或不符合伦理道德时。电子病历 然而，系统通常被开发为支持临床和/或计费活动。因此，实质性的护理 在使用电子病历数据来解决特定的科学fic问题时需要。在这方面，一个重要的潜在威胁 TO有效性缺少数据。此外，由于没有为任何特定的研究问题收集电子健康记录数据，它 通常情况下，对于回答问题至关重要的度量在 一些病人的病历。这反过来又要求研究人员与选择偏见和 泛化能力受损。 为了解决电子病历中数据缺失的问题，研究人员原则上可以呼吁 统计文献，并使用标准方法，如多重归因(MI)、逆概率加权 (IPW)或双稳健(DR)估计。然而，这些方法通常是在 电子病历上下文。因此，他们通常没有认识到电子病历数据的复杂性，特别是许多 由患者和医疗保健提供者做出的决定，在电子病历中产生“完整数据”，称为 数据的出处。因为这种复杂性与大多数缺失的设置之间的脱节 开发了数据方法，将标准缺失数据方法应用于基于电子病历的研究将经常 未能解决选择偏差和通用性仍将受到影响。 不幸的是，与混淆偏见相反，很少有人关注开发用于 丢失的数据是专门为基于电子病历的研究的复杂性量身定做的fi。我们将开始解决这一问题 通过开发、实施和评估一套新颖、创新的统计工具，包括：目标1：a Unified框架用于非匹配和匹配EHR队列研究中的稳健因果推断 混杂数据；目标2：在基于电子病历的模拟靶标试验中进行因果推断的正式、稳健的框架 数据；目标3：一种新的混合分析框架，用于基于EHR的研究中的缺失数据，该框架结合了MI和 IPW以创新和独特的方式；目标4：一种新的双采样策略，用于在电子病历数据被 怀疑是失踪的-不是随机的。 拟议目标的动机是调查团队在一系列以电子病历为基础的 对接受减肥手术的患者的长期结果的研究。在整个研究过程中， 我们将使用其中一项研究的数据，这项持久研究具有丰富的人口统计学和纵向数据 来自三个Kaiser Permanente医疗系统的≈45,000名接受减肥治疗的患者的临床信息 在1997年至2015年期间，有1,636,000名非手术患者参加了≈手术，在此期间，有1,636,000名非手术参与者参加了手术。

PROJECT SUMMARY Electronic health record (EHR) data represent a huge opportunity for cost-efficient clinical and public health research, especially when a randomized trial or a prospective observational study is not feasible or ethical. EHR systems, however, are typically developed to support clinical and/or billing activities. As such, substantial care is needed when using EHR data to address a particular scientific question. In this, an important potential threat to validity is missing data. Moreover, since EHR data are not collected for any particular research question, it will often be the case that measurements that are critical to answering the question will be unavailable in the record of some patients. This, in turn, requires researchers to contend with the potential for selection bias and compromised generalizability. Towards addressing issues of missing data in an EHR, researchers could, in principle, appeal to a vast statistical literature and use standard methods such as multiple imputation (MI), inverse-probability weighting (IPW) or doubly- robust (DR) estimation. These methods, however, have generally been developed outside of the EHR context. As such, they typically fail to acknowledge the complexity of the EHR data, in particular the many decisions made by patients and health care providers that give rise to `complete data' in the EHR, known to as the data provenance. Because of the disconnect between this complexity and the settings for which most missing data methods are developed, the application of standard missing data methods to EHR-based studies will often fail to resolve selection bias and generalizability will remain compromised. Unfortunately, in contrast to confounding bias, very little attention has been paid to developing methods for missing data that are specifically tailored to the complexity of EHR-based studies. We will begin to address this gap by developing, implementing and evaluating a suite of novel, innovative statistical tools including: Aim 1: A unified framework for robust causal inference in unmatched and matched EHR-based cohort studies with missing confounder data; Aim 2: A formal, robust framework for causal inference in emulated target trials based on EHR data; Aim 3: A novel blended analysis framework for missing data in EHR-based studies that combines MI and IPW in an innovative and unique way; Aim 4: A novel double-sampling strategy for when the EHR data are suspected to be missing-not-at-random. The proposed aims are motivated by challenges the investigative team has faced in a series of EHR-based studies of long-term outcomes among patients who have undergone bariatric surgery. Throughout this research, we will use data from one of these studies, the DURABLE study, which has rich demographic and longitudinal clinical information from three Kaiser Permanente health systems on ≈45,000 patients who underwent bariatric surgery between 1997-2015, as well as on ≈1,636,000 non-surgical enrollees during that time period.