Abstract
翟映红,陈琪,韩贺东,赵欣欣,高永晴,周详,贺佳.联合模型介绍及在医学研究中的应用[J].Chinese journal of Epidemiology,2019,40(11):1456-1460
联合模型介绍及在医学研究中的应用
Introduction of joint model and its applications in medical research
Received:May 24, 2019  
DOI:10.3760/cma.j.issn.0254-6450.2019.11.021
KeyWord: 联合模型  纵向数据  生存数据
English Key Word: Joint model  Longitudinal data  Survival data
FundProject:国家重点研发计划-前列腺癌专病队列研究(2017YFC0908005);上海市卫健委临床研究专项(20184Y0054);上海市青年科技英才扬帆计划(19YF1459200)
Author NameAffiliationE-mail
Zhai Yinghong Tongji University School of Medicine, Shanghai 200092, China  
Chen Qi Department of Health Statistics, The Second Military Medical University, Shanghai 200433, China  
Han Hedong Department of Health Statistics, The Second Military Medical University, Shanghai 200433, China  
Zhao Xinxin Tongji University School of Medicine, Shanghai 200092, China  
Gao Yongqing Tongji University School of Medicine, Shanghai 200092, China  
Zhou Xiang Department of Health Management, Anhui Medical Universite, Hefei 230032, China  
He Jia Tongji University School of Medicine, Shanghai 200092, China
Department of Health Statistics, The Second Military Medical University, Shanghai 200433, China 
hejia63@yeah.net 
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Abstract:
      医学随访研究中,纵向数据和生存数据往往伴随出现,并且通常相关联,对纵向数据和生存数据单独分析可能会导致有偏倚的结果。联合模型同时处理纵向数据及生存数据,能够纠正偏差,提高参数估计的效率并提供有效的推论,是目前医学研究中一个比较热门的方法。联合模型已取得较多进展,国内介绍及应用联合模型的文章较少。本文将从主要思想、基本框架、参数估计方法等方面对联合模型中的随机效应模型进行介绍,并基于R软件中程序包JM对艾滋病数据集分析,旨在阐明联合模型在处理医学随访数据中的优势,促进联合模型在临床研究中的应用。
English Abstract:
      In medical follow-up studies, longitudinal data and survival data are often accompanied and associated with each other, thus respective analysis of longitudinal and survival data might lead to biased results. Joint model can correct deviations, improve the efficiency of parameter estimation and provide effective inferences by simultaneously processing longitudinal and survival data. It is a popular method in medical research. Joint model has made much progress, whereas the literature about the joint model and its application is limited in China. This paper summarizes the main idea, basic framework, parameter estimation methods of random effect joint model and introduces the analysis on AIDS data set based on the R software package ‘JM’ to clarify the advantages of the joint model in processing medical follow-up data and promote the use of the joint model in clinical research.
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