Abstract
项永兵,高玉堂.分组Cox模型及其在癌症预后因素研究中的应用[J].Chinese journal of Epidemiology,1994,15(1):46-50
分组Cox模型及其在癌症预后因素研究中的应用
Grouped Cox Regression Model and Its Application in Study of Prognostic Factors on Cancer
Received:May 12, 1993  Revised:August 24, 1993
DOI:
KeyWord: 比例危险回归模型  分组Cox模型  广义线性模型  肺癌
English Key Word: Proportional hazards regression model  Grouped Cox model  GLM  Lung cancer
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Author NameAffiliation
Xiang Yongbing Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032 
Gao Yutang Department of Epidemiology, Shanghai Cancer Institute, Shanghai 200032 
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Abstract:
      Cox比例危险回归模型是医学随访研究、临床试验研究中分析生存资料最常用的多因素分析方法,但它不适合于处理分组生存资料或重叠严重的大样本生存数据。笔者对分组比例危险回归模型及其在大样本寿命表生存资料分析中的应用进行了讨论。最后结合实例借助于GLIM软件探讨它在肺癌随访资料预后因素分析中的应用。
English Abstract:
      Cox proportional hazards regression model is the most popular multivariate regression model for analysis of survival data in medical follow-up studies and clinical trials, but it is unable to handle grouped survival data or large data sets with many tied failure times adequately. This paper explores the grouped proportional hazards regression model (GPH model) and its use in analysis of large data sets presented in life tables. By use of the data in a lung cancer follow-up study conducted in urban area of Shanghai, the authors give an example in detail for analysing prognostic factors of lung cancer by using GLIM.
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