当前位置: X-MOL 学术Commun. Stat. Simul. Comput. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Regression analysis of the illness-death model with a shared frailty when all transition times are interval censored
Communications in Statistics - Simulation and Computation ( IF 0.9 ) Pub Date : 2020-12-01 , DOI: 10.1080/03610918.2020.1853165
Jinheum Kim 1 , Jayoun Kim 2 , Seong W. Kim 3
Affiliation  

Abstract

In biomedical or clinical studies, semi-competing risks data are often encountered in which one type of event may censor an other event, but not vice versa. An illness-death model is proposed to analyze these semi-competing risks data in the presence of interval censoring on both intermediate and terminal events. The Cox proportional hazards model is employed with a frailty effect to incorporate a dependent structure between non-fatal and fatal events and individual-specific variations as well. Weight allocations on sub-intervals of censored intervals are used to construct the modified likelihood functions. Marginalization of the full likelihood is accomplished using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through the iterative quasi-Newton algorithm. The proposed methodology is illustrated with several simulation studies and real data.



中文翻译:

所有过渡时间区间截尾时具有共同脆弱性的疾病-死亡模型的回归分析

摘要

在生物医学或临床研究中,经常会遇到半竞争风险数据,其中一种事件可能会审查另一种事件,但反之则不然。提出了一种疾病-死亡模型,用于在对中间事件和终端事件进行区间审查的情况下分析这些半竞争风险数据。采用具有脆弱效应的 Cox 比例风险模型,以纳入非致命和致命事件以及个体特定变异之间的依赖结构。删失区间的子区间上的权重分配用于构建修改后的似然函数。全似然的边缘化是通过自适应重要性采样来完成的,回归参数的最优解是通过迭代拟牛顿算法来实现的。

更新日期:2020-12-01
down
wechat
bug