纵向数据与生存数据联合模型中多变点识别问题

删除或更新信息，请邮件至freekaoyan#163.com(#换成@)

本站小编 Free考研考试/2024-01-16

-->

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].,2016,56(5):539-545

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 joint modeling of longitudinal and survival data

DOI：10.7511/dllgxb201605015

中文关键词: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

英文关键词:multiple change points linear mixed-effect (LME) model accelerated failure time (AFT) model joint inference maximum likelihood

基金项目:国家社会科学基金资助项目(16BGL060)；国家自然科学基金资助项目(11371077).

作者	单位
沈佳坤,宋立新,孙秀峰,冯宝军

摘要点击次数:1252

全文下载次数:1329

中文摘要:

提出了共享协变量和随机效应的纵向响应中含有多个变点识别的线性混合效应(LME)模型和加速失效时间(AFT)模型的联合模型，并通过Gauss-Hermite近似解决极大似然函数中的复杂积分以得到参数的估计．通过模拟研究验证了该方法的有效性，并将其应用于原发性胆汁性肝硬化(PBC)病变过程，研究发现 PBC患者的血清胆红素只在初期治疗阶段有所降低，两个月之后迅速开始反弹，直到3.5 a后增速才有所放缓，说明治疗方法仍需改进．

英文摘要:

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.

查看全文查看/发表评论下载PDF阅读器

关闭

纵向数据与生存数据联合模型中多变点识别问题

本站小编 Free考研考试/2024-01-16

相关话题/

领限时大额优惠券,享本站正版考研考试资料!