Effects of Missing Data Strategies on Disparities Research Results in HCUP SID

HCUP SID 中缺失数据策略对差异研究结果的影响

• 批准号: 8578389
• 负责人: Yan Ma
• 金额: $ 25万
• 依托单位: HOSPITAL FOR SPECIAL SURGERY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2014-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8578389
• 关键词: Effects; Missing; Data; Strategies; Disparities

Title: Effects of Missing Data Strategies on Disparities Research Results in HCUP SID Project Summary/Abstract Eliminating healthcare disparities so underserved communities (e.g., minorities, elderly, low income) and other AHRQ priority populations are assured access to quality medical care remains a national priority. Large, population based studies necessary to address healthcare disparities can be costly and difficult to perform, and may be compromised by sampling strategies and patient selection biases, an efficient alternative that is becoming increasingly attractive is the use of the Healthcare Cost & Utilization Project (HCUP) State Inpatient Databases (SID). A significant limitation of SID and other large databases is the quantity of missing data. In particular, "patient race", a key indicator for health disparities research, has a high proportion of missingness. The goal of this study is to make SID a more useful and reliable resource for the study of racial disparity. Accordingly, two multiple imputation (MI) methods (1) the sequential regression multivariate imputation, and (2) the latent normal multivariate imputation are proposed for addressing the missing data issue in the SID. These approaches will be compared through a comprehensive simulation study. Their advantages over the three commonly used missing data approaches (i.e. complete case analysis, missing indicator method, hot deck imputation) will also be illustrated through the simulation study. Based on the simulation, we will select the optimal MI method for imputation. As a result, multiply imputed datasets will be generated as a companion to the SID that will allow users to perform analysis using existing software for complete data for a wide range of substantive research questions. We will use imputed SID data to conduct musculoskeletal healthcare disparities research. The application is to determine whether race is a risk factor for set of adverse outcomes after total knee replacement (TKR) and whether racial disparities exist in utilization of TKR.

标题：缺失数据策略对HCUP SID中差异研究结果的影响 项目总结/摘要 消除医疗保健差距，使服务不足的社区（例如，少数民族、老年人、低收入者）和其他 保证AHRQ优先人口获得优质医疗服务仍然是国家优先事项。大号、 解决医疗保健差异所需的基于人群的研究可能成本高昂且难以执行， 可能会受到采样策略和患者选择偏差的影响，这是一种有效的替代方案， 越来越有吸引力的是使用医疗保健成本和利用项目（HCUP）国家住院病人 数据库（SID）。SID和其他大型数据库的一个重要限制是丢失数据的数量。在 特别是，“病人种族”，健康差距研究的一个关键指标，有很高的比例失踪。 本研究的目的是使SID成为研究种族差异的更有用和可靠的资源。 因此，两种多重插补（MI）方法（1）序贯回归多变量插补，（2） 提出了潜在正态多元插补的方法来解决SID中的数据缺失问题。这些 将通过一项全面的模拟研究对各种办法进行比较。他们的优势超过了三个 常用的缺失数据方法（即完整案例分析、缺失指标方法、热板 插补）也将通过模拟研究加以说明。基于模拟，我们将选择 最佳MI插补方法。因此，将生成多重插补数据集，作为 SID将允许用户使用现有软件对广泛的完整数据进行分析， 实质性的研究问题。我们将使用插补的SID数据进行肌肉骨骼保健 差距研究。该应用程序旨在确定种族是否是一组不良结局的风险因素 全膝关节置换术（TKR）后，以及TKR的使用是否存在种族差异。