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.

有几个不同的主题可以用多元失效时间回归数据来解决。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，当故障时间相关性本身很少或没有兴趣时。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程度。这些模型与脆弱模型重叠，似乎最适合探索故障时间聚类的细节。另一方面，最近提出的多元边际风险方法非常适合探索与单一、成对、和更高的维度危险率。这里将简要介绍这些方法，并使用女性健康倡议激素治疗试验数据说明最终方法。