Tang Lu, Zhou Ling, Song Peter X K
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
Comput Stat. 2019 Mar;34(1):395-414. doi: 10.1007/s00180-018-0827-6. Epub 2018 Jul 17.
We propose a fusion learning procedure to perform regression coefficients clustering in the Cox proportional hazards model when parameters are partially heterogeneous across certain predefined subgroups, such as age groups. One major issue pertains to the fact that the same covariate may have different influence on the survival time across different subgroups. Learning differences in covariate effects is of critical importance to understand the model heterogeneity resulted from the between-group heterogeneity, especially when the number of subgroups is large. We establish a computationally efficient procedure to learn the heterogeneous patterns of regression coefficients across the subgroups in Cox proportional hazards model. Utilizing a fusion learning algorithm coupled with the estimated parameter ordering, the proposed method mitigates greatly computational burden with little loss of statistical power. Extensive simulation studies are conducted to evaluate the performance of our method. Finally with a comparison to some popular conventional methods, we illustrate the proposed method by a vehicle leasing contract renewal analysis.
我们提出了一种融合学习程序，用于在参数在某些预定义亚组（如年龄组）之间部分异质的情况下，在Cox比例风险模型中进行回归系数聚类。一个主要问题在于，同一协变量在不同亚组中对生存时间可能有不同影响。了解协变量效应的差异对于理解由组间异质性导致的模型异质性至关重要，尤其是当亚组数量很大时。我们建立了一种计算效率高的程序，以了解Cox比例风险模型中亚组间回归系数的异质模式。利用融合学习算法并结合估计参数排序，所提出的方法在统计功效损失很小的情况下极大地减轻了计算负担。进行了广泛的模拟研究以评估我们方法的性能。最后，与一些流行的传统方法进行比较，我们通过车辆租赁合同续签分析说明了所提出的方法。
