Data Driven Methods for Missing Data Imputation in Surgical Disparities Research

项目摘要/摘要 在美国，医疗和医疗保健方面的差距一直是一个长期存在的挑战。一个特定的 已发现种族/民族差异的医疗领域是全关节置换术(TJA)， 尤其是全膝关节置换(TKA)和全髋关节置换(THA)。大规模、基于人群的研究 解决医疗保健差异所需的成本可能会很高，而且很难执行，而且可能会受到 抽样策略和患者选择偏差。高效的替代方案在全国范围内公开提供 具有代表性的数据库，如HCUP州住院患者数据库(SID)和全国住院患者样本 (NIS)。SID提供了所有在参与州医院住院的患者的信息，允许 比较许多弱势群体、跨州和不同时间的卫生保健可获得性。国家情报局 是全国最大的可公开使用的全付费住院患者医疗保健数据库。它是从 SID通过复杂的调查设计，得出国家对卫生保健利用率、质量和 结果。NIS和SID的一个重要限制是丢失数据的数量。尤其是“病人” “种族”是健康差距研究的一个关键指标，有很高的缺失率。多重归责 (MI)提供可靠的统计方法来解释失踪人数的做法越来越受欢迎 数据。在进行MI时，建议在数据允许的情况下尽可能普遍地使用归因模型 以适应对推定数据集的广泛后续分析。这需要所有 将在任何后续分析中调查的关系，例如非线性和 相互作用，将包括在归责模型中。不幸的是，传统的MI方法，如 链式方程(MICE)的多元填充是建立在参数填充模型上的。这些型号 往往不够灵活，无法捕获高维和大规模数据中的交互和非线性 设置。与参数模型不同，机器学习技术(MLT)是无模型的方法，因此 为缺失的数据补充提供灵活性。MLT使用自动和迭代学习的算法 所有数据用于检测观测中的统计相关性，而无需明确编程查看何处。 这项研究的目的是使两个HCUP数据库成为外科研究的更有用的资源 差异和其他医学领域。因此，我们提出了一种新的基于MLTS的MI方法 SID和NIS中缺失的数据，并使用推定的数据集来衡量TKA中的种族差异。

Project Summary/Abstract Disparities in health and health care have been a longstanding challenge in the United States. One specific area of medical care in which racial/ethnic disparities have been identified is total joint arthroplasty (TJA), particularly total knee arthroplasty (TKA) and total hip arthroplasty (THA). 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. Efficient alternatives are publicly-available nationally representative databases such as the HCUP State Inpatient Databases (SID) and National Inpatient Sample (NIS). The SID provide information on all patients admitted to hospitals within participating states, allowing for comparison of health care access among many vulnerable populations, across states, and over time. The NIS is the largest publicly-available all-payer inpatient health care database in the nation. It is sampled from the SID through a complex survey design, yielding national estimates of health care utilization, quality, and outcomes. A significant limitation of the NIS and the SID is the quantity of missing data. In particular, “patient race”, a key indicator for health disparities research, has a high proportion of missingness. Multiple imputation (MI) approaches have been increasingly popular for providing sound statistical methods to account for missing data. When conducting MI, it is suggested that imputation models be as general as data allow them to be, in order to accommodate a wide range of subsequent analyses of imputed data sets. This requires all relationships that are going to be investigated in any subsequent analysis, such as nonlinearities and interactions, to be included in the imputation model. Unfortunately, traditional MI methods, such as the multivariate imputation by chained equations (MICE), are built on parametric imputation models. These models are often not flexible enough to capture interactions and nonlinearities in high dimensional and large scale data settings. Unlike parametric models, machine learning techniques (MLTs) are model-free methods, and thus provide flexibility for missing data imputation. MLTs use algorithms that automatically and iteratively learn from all data to detect statistical dependencies in observations without being explicitly programmed where to look. The goal of this study is to make the two HCUP databases a more useful resource for the study of surgical disparities and other areas of medicine. Accordingly, we propose novel MI methods based on MLTs to impute missing data in the SID and the NIS, and to use the imputed datasets to measure racial disparity in TKA.