The stratified proportional hazards model represents a simple solution to take into account heterogeneity within the data while keeping the multiplicative effect of the predictors on the hazard function. Strata are typically defined a priori by resorting to the values of a categorical covariate. A general framework is proposed, which allows the stratification of a generic accelerated lifetime model, including, as a special case, the Weibull proportional hazards model. The stratification is determined a posteriori, taking into account that strata might be characterized by different baseline survivals, and also by 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. An optimal stratification is then identified following a decision theoretic approach. In turn, stratum-specific inference is carried out. The performance of this method and its robustness to the presence of right-censored observations are investigated through an extensive simulation study. Further illustration is provided analysing a data set from the University of Massachusetts AIDS Research Unit IMPACT Study.

分层比例风险模型代表了一种简单的解决方案，可以考虑数据内的异质性，同时保持预测变量对风险函数的乘法效应。Strata 通常是通过诉诸分类协变量的值来先验定义的。提出了一个通用框架，它允许对通用加速寿命模型进行分层，包括作为特例的 Weibull 比例风险模型。分层是事后确定的，考虑到地层可能具有不同的基线存活率，以及预测变量的不同影响。这是通过考虑贝叶斯非参数混合模型及其在数据分区空间上引起的后验分布来实现的。然后根据决策理论方法确定最佳分层。反过来，执行特定层的推理。通过广泛的模拟研究，研究了该方法的性能及其对右删失观测值的鲁棒性。进一步说明分析来自马萨诸塞大学艾滋病研究单位 IMPACT 研究的数据集。