摘要
精准医疗强调了正确识别异质性子群的重要性,以发展可针对每个子群的个性化治疗方案.尽管近来在子群分析的方法上取得了一些进展,在数据存在删失时,如何有效识别子群仍然缺乏探索.在本文中,我们提出了一种基于加速失效模型的新的子群分析方法,其中由潜在因素导致的异质性可以用特定于个体的截距项来表示.我们考虑最常见的右删失情况,并利用平均插补法对删失数据进行处理.硬阈值惩罚函数被应用于配对截距项的成对差值,可以自动地将观察个体划分为不同的子群.我们也建立了所提出的估计量的理论性质.模拟研究和威斯康辛乳腺癌数据集分析进一步验证了所提方法的有效性.
Precision medicine emphasizes the importance of correctly identifying heterogeneous sub-groups to develop individualized treatment strategies that can be prescribed for each subgroup.Despite the recent methodological advances in subgroup analysis,how to effectively identify subgroups for cen-sored data remains largely unexplored.In this paper,we propose a new methodology for subgroup analysis based on the accelerated failure time model,in which the heterogeneity caused by latent factors can be represented by subject-specific intercepts.We consider the most common right censoring situation,and processes the censored data by the mean imputation method.The hard thresholding penalty function is applied to pairwise differences of the intercepts,thus automatically dividing the observations into different subgroups.We also establish the theoretical properties of our proposed estimator.Our proposed method is further illustrated by simulation studies and analysis of a wisconsin breast cancer dataset.
作者
许赵辉
郑泽敏
吴捷
XU Zhaohui;ZHENG Zemin;WU Jie(School of Management,University of Science and Technology of China,Hefei,230026,China;School of Big Data and Statistics,Anhui University,Hefei,230601,China)
出处
《应用概率统计》
CSCD
北大核心
2023年第5期765-780,共16页
Chinese Journal of Applied Probability and Statistics
基金
国家重点研发计划项目(批准号:2022YFA1008000)
国家自然科学基金项目(批准号:72071187、1167-1374、 71731010、 12101584、 71921001)
中央高校基础研究经费(批准号:WK3470000017、 WK204000-0027)
中国博士后科学基金项目(批准号:2021TQ0326、2021M703100)
安徽大学博士科研启动基金项目(批准号:020318033/005)资助。
关键词
异质性加速失效模型
精确医疗
子群识别
删失数据
平均插补
硬阈值惩罚
heterogeneous accelerated failure time model
precision medicine
subgroup identification
censored data
mean imputation
hard thresholding penalty
