In the analysis of censored survival data, to avoid a biased inference of treatment effects on the hazard function of the survival time, it is important to consider the treatment heterogeneity. Without requiring any prior knowledge about the subgroup structure, we propose a data driven subgroup analysis procedure for the heterogeneous Cox model by constructing a pairwise fusion penalized partial likelihood‐based objective function. The proposed method can determine the number of subgroups, identify the group structure, and estimate the treatment effect simultaneously and automatically. A majorized alternating direction method of multipliers algorithm is then developed to deal with the numerically challenging high‐dimensional problems. We also establish the oracle properties and the model selection consistency for the proposed penalized estimator. Our proposed method is evaluated by simulation studies and further illustrated by the analysis of the breast cancer data.

中文翻译：

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异构Cox模型中的亚组分析

在对经过审查的生存数据进行分析时，为了避免对生存时间的危害函数对治疗效果的偏见，重要的是要考虑治疗的异质性。在不需要任何有关子组结构的先验知识的情况下，我们通过构建基于成对融合惩罚部分偏似性的目标函数，为异构Cox模型提出了一种数据驱动的子组分析程序。所提出的方法可以确定亚组的数量，确定组的结构，并同时自动地估计治疗效果。然后，开发了一种主要的乘数交替方向算法，以处理数值上具有挑战性的高维问题。我们还为拟定的惩罚估计量建立了预言性和模型选择一致性。