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

描述（由申请人提供）：消除医疗保健差距，使服务不足的社区（例如，确保少数民族、老年人、低收入者和其他AHRQ优先人口获得优质医疗服务仍然是国家优先事项。解决医疗保健差异所需的大型基于人群的研究可能成本高昂且难以执行，并且可能会受到抽样策略和患者选择偏差的影响，一种越来越有吸引力的有效替代方案是使用医疗保健成本与利用项目（HCUP）国家住院患者数据库（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。特别是“病人种族”，一个健康差异研究的关键指标，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，提出了两种多重填补（MI）方法（1）序贯回归多元填补，（2）潜在正态多元填补，以解决SID中的缺失数据问题。将通过全面的模拟研究对这些方法进行比较。通过模拟研究，还将说明它们相对于三种常用的缺失数据方法（即完整案例分析、缺失指标法、热甲板插补）的优势。根据模拟结果，我们将选择最佳的MI方法进行插补。因此，将生成多重估算数据集，作为SID的配套，使用户能够使用现有软件对广泛的实质性研究问题的完整数据进行分析。我们将使用估算的SID数据进行肌肉骨骼健康差异研究。本申请旨在确定种族是否是全膝关节置换术（TKR）后一系列不良结局的风险因素，以及TKR的使用是否存在种族差异。

DESCRIPTION (provided by applicant): 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 & Utilizatio 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 healt 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.