Statistical Methods for Complex Data in Cardiovascular Disease

心血管疾病复杂数据的统计方法

• 批准号: 8481622
• 负责人: Sean M O'Brien
• 金额: $ 37.1万
• 依托单位: NORTH CAROLINA STATE UNIVERSITY RALEIGH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8481622
• 关键词: Accounting; Address; Advocate; Area; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Characteristics; Chronic Disease; Clinical; Clinical Research; Code; Collaborations; Complex; Complication; Computer software; Conflict (Psychology); Confounding Factors (Epidemiology); Coronary Artery Bypass; Data; Data Set; Databases; Disease; Event; Goals; Harvest; Hazard Models; Health Sciences; Institution; Intervention; Link; Medical; Medicare claim; Medicine; Methods; Modeling; Myocardial Infarction; Observational Study; Operative Surgical Procedures; Outcome; Patients; Play; Population; Procedures; Proportional Hazards Models; Randomized; Registries; Research; Research Institute; Research Personnel; Research Project Grants; Role; Sampling; Scheme; Scientist; Specific qualifier value; Statistical Methods; Techniques; Testing; Time; United States Centers for Medicare and Medicaid Services; Veins; Vital Status; Weight; Withdrawal; administrative database; base; clinical practice; comparative; comparative effectiveness; data registry; design; hazard; improved; indexing; interest; intervention effect; method development; novel; patient population; percutaneous coronary intervention; public health relevance; theories

DESCRIPTION (provided by applicant): State-of-the-art cardiovascular disease (CVD) research presents novel, complex data-analytic challenges. This project will develop new statistical methods for such problems, motivated by the investigators' involvement in numerous CVD studies, that either break new ground, addressing issues for which no principled approaches exist, or that offer improvement over existing techniques. Many CVD studies seek to compare intervention-specific survival distributions using large observational databases. The objective of the first two aims is to develop new, optimal methods for estimating and comparing survival distributions in this setting, where the time-to-event out- come of interest may be censored, that take appropriate account of the confounding inherent in these data. The first aim is to derive optimal estimators for the survival distribution, the difference in treatment-specific survival distributions, and the hazard ratio for two treatments in a proportional hazards model. The estimators will rely on postulated models for the propensity of treatment, the censoring distribution, and the survival distribution as functions of patient covariates and will be "doubly robust" in the sense that they will be consistent for the true quantities even if subsets of these models are misspecified. In some settings, the data are obtained from vast registries where it is infeasible to collect on all subjects the detailed covariate information needed to adjust appropriately for confounding. A stratified sample that deliberately over-represents important subsets of the patient population may be obtained, from whom rich information on potential confounding variables is collected. The second aim is to develop such doubly robust estimators for the survival distribution under this complex sampling design. The goal of many CVD studies is to compare treatments on the basis of a composite time-to-event endpoint such as time to myocardial infarction or death (whichever comes first). However, some subjects may withdraw from the study before the composite endpoint may be ascertained, rendering it censored at the time of withdrawal. However, vital status for all subjects may be obtained at the end of the study from the national death indices, so that, for subjects who withdraw, additional information on one component of the composite is available. The third aim is to develop new methods for exploiting this information to obtain more precise estimators of and more powerful tests regarding treatment-specific survival distributions for the composite endpoint. A key challenge when linking administrative databases is the potential for information on intervention to be unreliable or conflicting; e.g., in a study to compare endoscopic vs. open vein graft harvesting in patients undergoing coronary artery bypass graft surgery, Medicare claims data may misclassify the technique used in some pro- portion of patients. The fourth aim is to develop improved methods for comparison of interventions based on a censored time-to-event outcome in this setting. Across all aims, the methods address problems both unique to CVD research and common in other chronic disease settings; thus, the latter will be broadly translatable across many disease areas.

描述（由申请人提供）：最先进的心血管疾病（CVD）研究提出了新颖、复杂的数据分析挑战。该项目将为此类问题开发新的统计方法，受到研究人员参与大量 CVD 研究的推动，这些方法要么开辟新天地，解决不存在原则性方法的问题，要么对现有技术进行改进。许多心血管疾病研究试图使用大型观察数据库来比较特定干预措施的生存分布。前两个目标的目标是开发新的、最佳的方法来估计和比较这种情况下的生存分布，其中感兴趣的事件时间结果可能会被审查，并适当考虑这些中固有的混杂因素。数据。第一个目标是导出生存分布的最佳估计量，即治疗特定的差异 生存分布，以及比例风险模型中两种治疗的风险比。估计器将依赖于治疗倾向、审查分布和生存分布的假设模型作为患者协变量的函数，并且将是“双重稳健的”，因为它们将与真实数量保持一致，即使这些数量的子集模型指定错误。在某些情况下，数据是从庞大的登记处获得的，在这些登记处收集所有受试者所需的详细协变量信息以适当调整混杂因素是不可行的。可以获得故意过度代表患者群体的重要子集的分层样本，从中收集关于潜在混杂变量的丰富信息。第二个目标是在这种复杂的抽样设计下开发这种双稳健的生存分布估计器。许多 CVD 研究的目标是根据复合事件发生时间终点（例如心肌梗死或死亡时间（以先到者为准））来比较治疗方法。然而，一些受试者可能在确定复合终点之前退出研究，从而在退出时对其进行审查。然而，在研究结束时，可以从国家死亡指数中获得所有受试者的生命状况，因此，对于退出的受试者，可以获得有关综合数据某一组成部分的附加信息。第三个目标是开发新方法来利用这些信息，以获得关于复合终点的治疗特异性生存分布的更精确的估计量和更强大的测试。连接行政数据库时的一个关键挑战是干预信息可能不可靠或相互冲突；例如，在一项比较内窥镜与开放静脉移植物采集的研究中 对于接受冠状动脉搭桥手术的患者，医疗保险索赔数据可能会错误分类某些患者所使用的技术。第四个目标是开发改进的方法，根据这种情况下经过审查的事件发生时间结果来比较干预措施。在所有目标中，这些方法解决了 CVD 研究特有的问题和其他慢性病环境中常见的问题；因此，后者将广泛应用于许多疾病领域。