Statistical Methods for Complex Data in Cardiovascular Disease

描述（由申请人提供）：最先进的心血管疾病（CVD）研究提出了新的，复杂的数据分析挑战。该项目将为这些问题开发新的统计方法，其动机是研究人员参与了许多CVD研究，这些研究要么开辟新天地，解决不存在原则性方法的问题，要么提供对现有技术的改进。许多心血管疾病研究试图使用大型观察数据库来比较干预特定的生存分布。前两个目标的目的是开发新的最佳方法，用于估计和比较这种情况下的生存分布，其中可能对关注的至事件时间结局进行删失，并适当考虑这些数据中固有的混杂因素。第一个目的是获得生存分布的最佳估计，即治疗特异性 生存分布和比例风险模型中两种治疗的风险比。估计量将依赖于假设的治疗倾向模型、删失分布和生存分布作为患者协变量的函数，并且在即使这些模型的子集被错误指定，它们对于真实量也是一致的意义上是“双重稳健”的。在某些情况下，数据是从大量登记研究中获得的，在这些登记研究中，收集所有受试者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人群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。第二个目的是开发这样的双重稳健估计的生存分布下，这种复杂的抽样设计。许多CVD研究的目标是基于复合事件发生时间终点（如至心肌梗死或死亡的时间（以先发生者为准））比较治疗。然而，一些受试者可能在确定复合终点之前退出研究，从而在退出时删失。然而，所有受试者的生命状态可在研究结束时从国家死亡指数中获得，因此，对于退出研究的受试者，可获得关于复合终点中一个组分的额外信息。第三个目标是开发新的方法来利用这些信息，以获得更精确的估计和更强大的测试有关的治疗特异性生存分布的复合终点。在连接行政数据库时的一个关键挑战是，有关干预措施的信息可能不可靠或相互矛盾;例如，在一项比较内窥镜与开放静脉移植物采集的研究中， 对于接受冠状动脉旁路移植手术的患者，医疗保险声称数据可能会错误分类一些患者使用的技术。第四个目标是开发改进的方法，在这种情况下，基于删失的事件发生时间结局比较干预措施。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，后者将在许多疾病领域广泛翻译。

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.