The stratified proportional hazards model represents a simple solution to account for heterogeneity within the data while keeping the multiplicative effect on the hazard function. Strata are typically defined a priori by resorting to the values taken by a categorical covariate. A general framework is proposed, which allows for the stratification of a generic accelerated life time model, including as a special case the Weibull proportional hazard model. The stratification is determined a posteriori by taking into account that strata might be characterized by different baseline survivals as well as different effects of the predictors. This is achieved by considering a Bayesian nonparametric mixture model and the posterior distribution it induces on the space of data partitions. The optimal stratification is then identified by means of the variation of information criterion and, in turn, stratum-specific inference is carried out. The performance of the proposed method and its robustness to the presence of right-censored observations are investigated by means of an extensive simulation study. A further illustration is provided by the analysis of a data set extracted from the University of Massachusetts AIDS Research Unit IMPACT Study.


翻译:分层比例危害模型是一个简单的解决办法,既说明数据内部的异质性,又保留对危害功能的多倍效应。Strata通常是先验定义的,采用绝对共变的数值。提出一个总体框架,允许对通用加速寿命模型进行分层,包括作为特例的Weibull比例危害模型。分层是事后确定的,考虑到各层可能有不同的基线生存特征以及预测器的不同影响。这是通过考虑一种巴伊西亚非参数混合模型及其在数据分隔空间的外表分布而实现的。然后,通过信息标准的变化来确定最佳分层,并反过来通过分层特定推理进行。通过广泛的模拟研究对拟议方法的绩效及其对于正确观测的强度进行了调查。通过分析从麻省理大学艾滋病研究组IMPACT研究中提取的数据,提供了进一步说明。

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