Computational methods for case-cohort studies

被引:107
作者
Langholz, Bryan
Jiao, Jenny
机构
[1] Univ So Calif, Keck Sch Med, Dept Prevent Med, Los Angeles, CA 90089 USA
[2] Catalyst Pharmaceut Res LLC, Pasadena, CA 91105 USA
关键词
bias; Cox model; cumulative hazard; risk estimation; risk sets; stratified Cox model; time-dependent covariates;
D O I
10.1016/j.csda.2006.12.028
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Computational methods, which. can be implemented using standard Cox regression software, are given for fitting "exact" pseudo-likehood estimates and robust and asymptotic variance estimators from case-cohort data. These methods are based on the computational approach of Therneau and Li [1999. Computing the Cox model for case cohort designs. Lifetime Data Anal. 5, 99-112] but will be less subject to small sample bias. Further, it is shown how to accommodate time-dependent covariates and estimate absolute risk. Extensions to stratified case-cohort sampled data are also provided. The methods are illustrated in analyses of case-cohort samples from a study of radiation exposure from fluoroscopy and breast cancer using SAS software. (c) 2007 Elsevier B.V. All rights reserved.
引用
收藏
页码:3737 / 3748
页数:12
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