有几个不同的主题可以解决多变量故障时间回归数据。需要适合每个此类主题的数据分析方法。具体来说,边际危险率模型非常适合分析与单个故障时间结果相关的暴露或处理,当故障时间依赖性本身很少或根本不感兴趣时。另一方面,半参数copula模型非常适合分析,其中兴趣主要集中在故障时间之间的依赖性大小上。这些模型与脆弱模型重叠,这似乎最适合探索故障时间聚类的细节。最近提出的多元边际风险方法,另一方面,非常适合探索与单身有关的暴露或治疗方法,成对,和更高的维度危险率。这里将简要描述这些方法,最后的方法将使用妇女健康倡议激素治疗试验数据进行说明。
There are several different topics that can be addressed with multivariate failure time regression data. Data analysis methods are needed that are suited to each such topic. Specifically, marginal hazard rate models are well suited to the analysis of exposures or treatments in relation to individual failure time outcomes, when failure time dependencies are themselves of little or no interest. On the other hand semiparametric copula models are well suited to analyses where interest focuses primarily on the magnitude of dependencies between failure times. These models overlap with frailty models, that seem best suited to exploring the details of failure time clustering. Recently proposed multivariate marginal hazard methods, on the other hand, are well suited to the exploration of exposures or treatments in relation to single, pairwise, and higher dimensional hazard rates. Here these methods will be briefly described, and the final method will be illustrated using the Women\'s Health Initiative hormone therapy trial data.