大连理工大学学报2016，Vol.56Issue(5)：539-545,7.DOI:10.7511/dllgxb201605015
纵向数据与生存数据联合模型中多变点识别问题
Multiple change points identification in j oint modeling of longitudinal and survival data
摘要
Abstract
A joint model with multiple change points identifying in longitudinal response process is proposed,which combines a linear mixed-effect (LME)model and an accelerated failure time (AFT) model with respect to shared covariates and random effects.All the parameters are estimated by the maximum likelihood function through the Gauss-Hermite approximation to deal with the intractable integrals in it.The effect of the method is elucidated through simulation studies and a real data application about primary biliary cirrhosis (PBC).It is shown that serum bilirubin level declines only at the beginning of treatment and lasts two months,then quickly rebounds and doesn't slow down until 3.5 years later,which indicates that the treatment methods still need to be improved.关键词
多变点/线性混合效应模型/加速失效时间模型/联合推断/极大似然Key words
multiple change points/linear mixed-effect (LME)model/accelerated failure time (AFT) model/j oint inference/maximum likelihood分类
数理科学引用本文复制引用
沈佳坤,宋立新,孙秀峰,冯宝军..纵向数据与生存数据联合模型中多变点识别问题[J].大连理工大学学报,2016,56(5):539-545,7.基金项目
国家社会科学基金资助项目(16BGL060) （16BGL060）
国家自然科学基金资助项目(11371077) （11371077）
