Association Analysis of Multivariate Competing Risks Data

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。 在这个建议中，研究者描述了三个项目的关联分析的多变量竞争风险的数据，经常出现在遗传家族研究，人口学和其他领域。 通常人们感兴趣的是某个事件的发生时间与竞争事件的存在的家族关联，竞争事件可能依赖于目标事件的发生。 多变量生存数据的常用关联方法假设独立于目标事件的竞争事件删失，可能会产生有偏倚的结果。 此外，关注事件的边际分布不可识别。 因此，多变量竞争风险数据的关联分析主要集中在竞争风险文献中的两个重要量：原因特异性危害（CSH）和累积发生率函数（CIF）。 研究者开发了一系列多变量竞争风险数据的关联分析，这些数据适当地解释了竞争事件的依赖性删失。 第一个项目是关于多变量竞争风险数据的两个等价的关联测度，它们是CSH比，并在没有平滑的情况下进行非参数估计。 在第二个项目中，研究者通过一个不适当的随机变量将脆弱性模型的应用扩展到多变量竞争风险数据的关联分析中，并以其边缘和关联参数表示双变量CIF。 为了纳入协变量，在第三个项目中，研究者开发了参数回归模型，以研究协变量对边际CIF的影响以及协变量对关联分析的间接影响。 这些关联方法涵盖了许多现有的双变量数据的特殊情况下的方法。 该提案集中在建模的目标事件与竞争事件的存在下，标准的方法可能会产生偏见的结果的家族关联。 例如，在一项大型痴呆症研究中，痴呆症发作的家庭聚集性是令人感兴趣的，其中竞争性事件死亡可能排除痴呆症的发生。 这项研究在痴呆症和其他健康和医学研究中的应用有望产生对集群成员之间目标事件关联的新见解，这有助于个人和从业者更准确地感知家族风险。 提高认识可能会导致更好的预防和干预的目标人群谁是高风险。该方法可用于许多其他应用，如人口研究的人类死亡率，极端的金融资产和回报，遗传评估的公牛寿命的奶牛和年金估值与依赖死亡率的保险。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). In this proposal, the investigator describes three projects on association analysis of multivariate competing risks data which arise frequently in genetic family studies, demography and other areas. Often one is interested in familial association of the onset time of a certain event, with the presence of competing events which may dependently censor the occurrence of the target event. The usual association methods for multivariate survival data assuming the censoring by competing events independent of the target event may produce biased results. In addition, the marginal distributions of the event of interest are not identifiable. Hence the proposed association analysis for multivariate competing risks data focuses on two important quantities in competing risks literature: cause-specific hazard (CSH) and cumulative incidence functions (CIFs). The investigator develops a series of association analyses of multivariate competing risks data which account for the dependence censoring by competing events appropriately. The first project is related to two equivalent association measures of multivariate competing risks data which are CSH ratios and estimated nonparametrically without smoothing. In the second project, the investigator expands the application of frailty models to association analysis of multivariate competing risks data through an improper random variable and expresses the bivariate CIF in terms of its marginals and an association parameter. To incorporate covariates, in the third project, the investigator develops parametric regression models to investigate covariate effects on marginal CIFs and the indirect effects of covariates on the association analysis. These association methods cover many existing approaches for bivariate data as special cases. The proposal concentrates on modeling familial association in a target event with the presence of competing events where the standard methods may produce biased results. For example, in a large dementia study, family clustering in dementia onset is of interest where the competing event death may preclude the occurrence of dementia. The application of this research to the dementia and other studies in health and medicine is expected to generate novel insights on the association in a target event among members of a cluster, which help individuals and practitioners perceive the familial risks more accurately. The enhanced understanding may lead to better prevention and intervention in the target population who are at elevated risks. The methods can be used in many other applications such as demographic studies of human mortality, extremes in financial assets and returns, genetic evaluation of sires for longevity of dairy cows and annuity valuation with dependent mortality in insurance.