摘要
在已有生存分析研究中,大多直接假设响应变量与指定协变量的模型形式,进而估计协变量效应,但当模型假设错误时,对应的结论可能是错误的。因此,为了避免指定协变量构建模型引起的不准确性,考虑使用一种基于模型平均方法的加速失效时间模型来对带治愈组的右删失数据进行刻画。在极大似然估计的框架下,采用基于信息准则的模型选择和模型平均方法进行统计推断研究。数值模拟结果显示,在带治愈组的右删失数据下基于模型平均方法的加速失效时间(accelerated failure time,AFT)模型估计及预测精度高于模型选择方法。最后通过黑色素瘤临床试验数据的分析,对所提方法的可行性和实用性进行验证。
In the existing survival analysis studies,most of them directly assume the model form of response variables and specified covariates,and then estimate the covariate effect.However,when the assumption of the model is wrong,the corresponding conclusion may be wrong.Under the right-censored data with a cured group,in order to avoid the inaccuracy caused by the construction of the model with specified covariates,an accelerated failure time model(AFT model)based on the model averaging method is proposed.In the framework of maximum likelihood estimation,model selection and model averaging based on information criteria are used for statistical inference.The numerical simulation results show that under the right-censored data with a cured group,the estimation and prediction accuracy of the AFT model based on the model averaging method is higher than that of model selection method.Finally,the feasibility and practicability of the proposed method are verified by analysis of trial data on melanoma clinical.
作者
王淑影
张亚男
程云飞
周丽芳
WANG Shuying;ZHANG Yanan;CHENG Yunfei;ZHOU Lifang(School of Mathematics and Statistics,Changchun University of Technology,Changchun 130012,Jilin,China)
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2024年第4期108-116,共9页
Journal of Shandong University(Natural Science)
基金
吉林省自然科学基金优秀青年基金项目(20230101371JC)。
关键词
右删失数据
模型平均
混合治愈模型
加速失效时间模型
极大似然估计
right-censored data
model averaging
mixture cure model
accelerated failure time model
maximum likelihood estimation
