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)提供资金的。在这项建议中，研究者描述了三个关于多变量竞争风险数据的关联分析的项目，这些数据经常出现在遗传家系研究、人口学和其他领域。通常，人们感兴趣的是某个事件的开始时间与可能依赖于审查目标事件的发生的竞争事件的存在的家族关联。对于多变量生存数据，通常的关联方法假设竞争事件独立于目标事件进行审查，可能会产生有偏差的结果。此外，感兴趣事件的边际分布是不可识别的。因此，所提出的多变量竞争风险数据的关联分析侧重于竞争风险文献中的两个重要变量：原因特定风险(CSH)和累积关联函数(CIF)。研究者对多变量竞争风险数据进行了一系列的关联分析，适当地解释了竞争事件对相关性的审查。第一个项目涉及多变量竞争风险数据的两个等价关联度量，即CSH比率和非参数估计，而不进行平滑。在第二个项目中，研究人员通过一个不适当的随机变量扩展了脆弱性模型在多变量竞争风险数据关联分析中的应用，并用它的边际和一个关联参数来表示双变量的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，以考察协变量对边际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二变量数据作为特例的方法。该建议集中于在存在竞争事件的目标事件中建模家庭关联，在这些竞争事件中，标准方法可能会产生有偏见的结果。例如，在一项大型痴呆症研究中，在相互竞争的事件死亡可能排除痴呆症发生的情况下，痴呆症发病的家庭聚集性是有意义的。将这项研究应用于痴呆症和其他健康和医学研究，有望对集群成员中目标事件的关联产生新的见解，帮助个人和从业者更准确地感知家庭风险。加强了解可能导致对处于高危状态的目标人群进行更好的预防和干预。该方法还可用于其他许多应用，如人类死亡率的人口统计学研究、金融资产和收益的极端情况、奶牛长寿的遗传评估以及保险中具有依赖死亡率的年金评估。

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.