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: 10589133
• 负责人: SEBASTIEN HANEUSE
• 金额: $ 51.8万
• 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/10589133
• 关键词: Address; Attention; Caring; Clinical; Cohort Studies; Complex; Data; Data Provenance; Decision Making; Electronic Health Record; Eligibility Determination; Ethics; Face; Health Personnel; Health system; Literature; Longitudinal Studies; Measurement; Methodology; Methods; Observational Study; Outcome; Patients; Probability; Research; Research Design; Research Personnel; Sampling; Selection Bias; Series; Specific qualifier value; Statistical Methods; Techniques; Time; bariatric surgery; cohort; cost effective; cost efficient; design; electronic health record system; 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.

项目概要 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机遇 研究，特别是当随机试验或前瞻性观察研究不可行或不符合伦理道德时。 然而，系统通常是为了支持临床和/或计费活动而开发的。 当使用 EHR 数据来解决特定的科学问题时，这是一个重要的潜在威胁。 此外，由于 EHR 数据不是针对任何特定研究问题收集的，因此缺乏数据。 通常情况下，对于回答问题至关重要的测量将无法在 反过来，这要求研究人员应对潜在的选择偏差和 损害了普遍性。 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广大 统计文献并使用多重插补 (MI)、逆概率加权等标准方法 然而，这些方法通常是在外部开发的。 因此，他们通常无法认识到 EHR 数据的复杂性，特别是许多数据。 患者和医疗保健提供者做出的决定会在电子病历中产生“完整数据”，称为 由于这种复杂性与大多数缺失的设置之间的脱节。 开发数据方法后，标准缺失数据方法在基于 EHR 的研究中的应用通常会 如果不能解决选择偏差，普遍性仍然会受到影响。 不幸的是，与混杂偏见相比，很少有人关注开发用于治疗的方法。 缺少专门针对基于电子病历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、创新的统计工具来缩小差距，包括： 目标 1：A 在未匹配和匹配的基于 EHR 的队列研究中进行稳健因果推断的统一框架 混杂数据；目标 2：基于 EHR 的模拟目标试验中因果推理的正式、稳健的框架 目标 3：针对基于 EHR 的研究中缺失数据的新颖混合分析框架，将 MI 和 以创新且独特的方式进行 IPW；目标 4：针对 EHR 数据的新颖双采样策略 疑似失踪——并非随机。 拟议目标的动机是调查团队在一系列基于电子病历的调查中面临的挑战 在整个研究中，对接受减肥手术的患者的长期结果进行了研究。 我们将使用其中一项研究的数据，即持久研究，该研究具有丰富的人口统计和纵向数据 来自三个 Kaiser Permanente 卫生系统的约 45,000 名接受减肥治疗的患者的临床信息 1997 年至 2015 年间接受过手术治疗，以及该时期约 1,636,000 名非手术参与者。