右删失数据下多响应AFT模型的两阶段估计  被引量:1

A Two-Stage Estimation of Multiple-Response AFT Model with Right-Censored Data

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作  者:刘慧馨 LIU Huixin(School of Data Science,University of Science and Technology of China,Hefei,230026,China)

机构地区:[1]中国科学技术大学大数据学院,合肥230026

出  处:《应用概率统计》2023年第1期10-26,共17页Chinese Journal of Applied Probability and Statistics

摘  要:在生存分析领域,加速失效时间(AFT)模型经常被用于预测事件发生的时间.本文将该模型推广到多事件时间情形,提出了多响应AFT模型,并假设协变量是高维的,模型的系数矩阵是联合低秩且稀疏的.此外还假设多个事件时间受制于同一个右删失变量.为了估计模型中的系数矩阵,本文提出一个两阶段方法,先对数据进行逆概率删失加权(IPCW),再用SESS算法求解一个稀疏降秩回归问题.本文通过数值模拟,验证了所提方法的有效性.最后将该方法应用于一个关于白血病患者骨髓移植的临床数据集.In survival studies,the accelerated failure time(AFT)model is often applied to predict the event times.This article proposes a multiple-response AFT model that extends the AFT model to the multiple events case.It is assumed that the covariates are high-dimensional and the regression coefficient matrix is jointly low-rank and sparse.We also assume all the multivariate event times are subject to right-censoring by a common censoring variable.To estimate the coefficient matrix,a two-stage procedure is proposed.First weight the data with IPCW weights,and then use SESS algorithm to solve a sparse reduced-rank regression problem.The simulation results show that the proposed method performs well in many cases.The method is also applied to a real dataset of bone marrow transplant patients.

关 键 词:多响应AFT模型 多元右删失数据 逆概率删失加权 SESS算法 生存分析 稀疏降秩回归 

分 类 号:O212.1[理学—概率论与数理统计] O212.4[理学—数学]

 

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