Data Driven Methods for Missing Data Imputation in Surgical Disparities Research

手术差异研究中缺失数据插补的数据驱动方法

• 批准号: 10424471
• 负责人: Yan Ma
• 金额: --
• 依托单位: GEORGE WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-24 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10424471
• 关键词: Address; Area; Caring; Companions; Complex; Data; Data Set; Data Sources; Databases; Decision Trees; Dependence; Discipline; Elderly; Electronic Health Record; Equation; Evaluation; Future; Gender; Goals; Health; Health Disparities Research; Healthcare; Hospitals; Hybrids; Inpatients; Knowledge; Lead; Learning; Length of Stay; Low income; Machine Learning; Measures; Medical; Medical Research; Medicine; Methods; Minority; Modeling; Musculoskeletal; Outcome; Patient Selection; Patients; Population Study; Race; Records; Replacement Arthroplasty; Research; Research Personnel; Resources; Sampling; Selection Bias; Statistical Methods; Surveys; Techniques; Time; United States; Validation; Vulnerable Populations; administrative database; autoencoder; automated algorithm; base; cost; design; flexibility; health care availability; health care disparity; health care service utilization; health disparity; high dimensionality; hip replacement arthroplasty; hospital readmission; improved; innovation; knee replacement arthroplasty; large scale data; mortality; mortality risk; neural network; novel; novel strategies; racial and ethnic disparities; racial disparity; random forest; simulation; software development; sound; success; surgery outcome; surgical disparities; tool; underserved community; usability

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.

项目总结/摘要 健康和医疗保健方面的差距一直是美国面临的一个长期挑战。一个具体 已确定存在种族/民族差异的医疗领域是全关节置换术（TJA）， 特别是全膝关节成形术（TKA）和全髋关节成形术（THA）。基于人群的大型研究 解决医疗保健差异的必要性可能是昂贵的，难以执行，并且可能会受到以下因素的影响： 抽样策略和患者选择偏差。有效的替代品可在全国范围内公开获得 代表性数据库，如HCUP州住院患者数据库（SID）和国家住院患者样本 （新谢克尔）。SID提供了参与国家内所有住院患者的信息， 比较许多弱势群体、各州和一段时间内获得医疗保健的情况。的NIS 是全国最大的公开提供的所有付费住院医疗保健数据库。它是从 SID通过一个复杂的调查设计，产生国家估计的卫生保健利用，质量， 结果。NIS和SID的一个重要限制是丢失数据的数量。特别是，“病人 “种族”是健康差异研究的一个关键指标，但缺失比例很高。多重插补 (MI)越来越多的方法提供了合理的统计方法来解释失踪人口， 数据在进行MI时，建议插补模型应尽可能通用， 以适应对插补数据集的广泛后续分析。这要求所有 在任何后续分析中将要研究的关系，例如非线性和 相互作用，将其纳入插补模型。不幸的是，传统的MI方法，如 多元插补链式方程（MICE），建立在参数插补模型。这些模型 通常不够灵活，无法捕捉高维和大规模数据中的相互作用和非线性 设置.与参数模型不同，机器学习技术（MLT）是无模型方法，因此 为缺失数据插补提供灵活性。MLT使用自动和迭代地从 所有数据都可以检测观察结果中的统计依赖性，而无需明确编程查看。 本研究的目的是使两个HCUP数据库成为外科手术研究的更有用的资源。 差异和其他医学领域。因此，我们提出了新的MI方法的基础上的MLT来填补 SID和NIS中的缺失数据，并使用插补数据集测量TKA中的种族差异。