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

基于电子健康记录的研究中缺失数据的稳健方法

• 批准号: 10181873
• 负责人: SEBASTIEN HANEUSE
• 金额: $ 56.68万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-12 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10181873
• 关键词: Address; Attention; Caring; Clinical; Cohort Studies; Complex; Data; Data Provenance; Electronic Health Record; Eligibility Determination; Ethics; Face; Health Personnel; Health system; Literature; Longitudinal Studies; Measurement; Methodology; Methods; Modeling; Observational Study; Outcome; Patient Care; Patients; Probability; Research; Research Design; Research Personnel; Sampling; Selection Bias; Series; Statistical Methods; System; Techniques; Time; Weight; bariatric surgery; base; cohort; cost effective; design; epidemiology study; experience; flexibility; innovation; novel; opportunity cost; prospective; public health research; randomized trial; semiparametric; tool

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.

项目摘要 电子健康记录（EHR）数据为具有成本效益的临床和公共卫生提供了巨大的机会 研究，特别是当随机试验或前瞻性观察性研究不可行或不符合伦理时。EHR 然而，系统通常被开发为支持临床和/或计费活动。因此，实质性护理 当使用EHR数据来解决特定的科学问题时，这是必要的。在这方面，一个重要的潜在威胁 是缺少数据。此外，由于EHR数据不是为任何特定的研究问题收集的， 通常情况下，对于回答问题至关重要的测量结果在 一些病人的记录。这反过来又要求研究人员应对潜在的选择偏差， 妥协的普遍性。 为了解决EHR中缺失数据的问题，研究人员原则上可以呼吁广泛的 统计文献，并使用标准方法，如多重插补（MI），逆概率加权 (IPW)或双稳健（DR）估计。然而，这些方法通常是在非专利领域开发的。 EHR上下文。因此，他们通常没有认识到EHR数据的复杂性，特别是许多 患者和医疗保健提供者做出的决定，这些决定在EHR中产生了“完整数据”，称为 数据来源。由于这种复杂性与大多数缺失的设置之间的脱节， 数据方法的发展，标准的缺失数据方法的应用，以EHR为基础的研究，往往 如果不能解决选择偏差，普遍性将继续受到损害。 不幸的是，与混杂偏倚相反，很少有人注意开发方法， 缺少专门针对基于EHR的研究的复杂性的数据。我们将开始解决这个问题 通过开发、实施和评估一套新颖、创新的统计工具，消除差距，包括： 在缺失的非匹配和匹配EHR队列研究中进行稳健因果推断的统一艾德框架 混杂数据;目标2：基于EHR的模拟靶试验中因果推断的正式，强大的框架 数据;目标3：一种新的混合分析框架，用于基于EHR的研究中缺失的数据， IPW以创新和独特的方式;目标4：一种新颖的双采样策略，当EHR数据 疑似非随机失踪 拟议的目标是出于调查小组在一系列基于电子健康记录的 在接受减肥手术的患者中进行的长期结局研究。在整个研究过程中， 我们将使用来自其中一项研究的数据，即DURABLE研究，该研究具有丰富的人口统计学和纵向数据， 来自三个Kaiser Permanente卫生系统的145，000名接受减肥治疗的患者的临床信息 1997年至2015年期间的手术，以及在此期间的1，636，000名非手术登记者。