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。该项目将为这类问题开发新的统计方法，其动机是调查人员参与许多心血管疾病研究，这些方法要么开辟新天地，解决没有原则性办法解决的问题，要么改进现有技术。许多心血管疾病研究试图使用大型观察性数据库来比较特定于干预的存活率分布。前两个目标的目的是开发新的、最优的方法来估计和比较这种情况下的生存分布，在这种情况下，感兴趣的事件产生的时间可能被审查，并适当地考虑到这些数据中固有的混杂。第一个目标是得出生存分布的最优估计值，即特定治疗的差异。 在比例风险模型中，两种处理的生存分布和风险比。估计者将依赖于治疗倾向、截尾分布和生存分布的假设模型作为患者协变量的函数，并将在即使这些模型的子集被错误指定的情况下它们对于真实量是一致的，这一意义上将是“双稳健”的。在一些情况下，数据是从庞大的登记处获得的，在那里无法收集关于所有受试者的详细协变量信息，以便对混淆进行适当的调整。可以获得故意过度代表患者群体重要子集的分层样本，从该样本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，发展生存分布的双稳健估计。许多心血管疾病研究的目标是根据复合的事件发生时间终点来比较治疗，例如心肌梗死或死亡的时间(以先发生者为准)。然而，一些受试者可能会在复合终点确定之前退出研究，使其在退出时被审查。然而，所有受试者的生命状况可以在研究结束时从国家死亡指数中获得，因此，对于退出的受试者，可以获得关于综合数据的一个组成部分的额外信息。第三个目标是开发利用这些信息的新方法，以获得对复合终点的治疗特定生存分布的更准确的估计值和更强大的检验。链接管理数据库时的一个关键挑战是关于干预的信息可能不可靠或相互冲突；例如，在一项比较内窥镜和开放静脉移植物采集的研究中 接受冠状动脉搭桥手术的患者，Medicare声称数据可能会错误地对某些比例的患者使用的技术进行分类。第四个目标是开发改进的方法，在这种情况下，根据审查后的事件发生时间结果比较干预措施。纵观所有目标，这些方法既解决了心血管疾病研究特有的问题，也解决了其他慢性病环境中常见的问题；因此，后者将广泛适用于许多疾病领域。

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.