Improve efficiency and reduce bias of Cox regression models for two-stage randomization designs using auxiliary covariates

doi:10.1002/sim.7252

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	Improve efficiency and reduce bias of Cox regression models for two-stage randomization designs using auxiliary covariates
	Yang, Xue 1,2; Zhou, Yong1,3
	2017-05-20
发表期刊	STATISTICS IN MEDICINE
ISSN	0277-6715
卷号	36 期号:11 页码:1683-1695
摘要	Two-stage randomization designs are broadly accepted and becoming increasingly popular in clinical trials for cancer and other chronic diseases to assess and compare the effects of different treatment policies. In this paper, we propose an inferential method to estimate the treatment effects in two-stage randomization designs, which can improve the efficiency and reduce bias in the presence of chance imbalance of a robust covariate-adjustment without additional assumptions required by Lokhnygina and Helterbrand (Biometrics, 63:422-428)'s inverse probability weighting (IPW) method. The proposed method is evaluated and compared with the IPW method using simulations and an application to data from an oncology clinical trial. Given the predictive power of baseline covariates collected in this real data, our proposed method obtains 17-38% gains in efficiency compared with the IPW method in terms of overall survival outcome. Copyright (C) 2017 John Wiley & Sons, Ltd.
关键词	two-stage randomization design inverse probability weighting Cox regression covariate adjustment semiparametric theory projection theorem
DOI	10.1002/sim.7252
语种	英语
资助项目	National Natural Science Foundation of China[71331006] ; State Key Program in the Major Research Plan of National Natural Science Foundation of China[91546202] ; National Center for Mathematics and Interdisciplinary Sciences (NCMIS), Key Laboratory of RCSDS, AMSS, CAS[2008DP173182] ; Innovative Research Team of Shanghai University of Finance and Economics[IRTSHUFE13122402]
WOS研究方向	Mathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Research & Experimental Medicine ; Mathematics
WOS类目	Mathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Medicine, Research & Experimental ; Statistics & Probability
WOS记录号	WOS:000400595100001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/26005
专题	应用数学研究所
通讯作者	Zhou, Yong
作者单位	1.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China 2.Janssen Res & Dev, Stat & Decis Sci, Shanghai, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Yang, Xue,Zhou, Yong. Improve efficiency and reduce bias of Cox regression models for two-stage randomization designs using auxiliary covariates[J]. STATISTICS IN MEDICINE,2017,36(11):1683-1695.
APA	Yang, Xue,&Zhou, Yong.(2017).Improve efficiency and reduce bias of Cox regression models for two-stage randomization designs using auxiliary covariates.STATISTICS IN MEDICINE,36(11),1683-1695.
MLA	Yang, Xue,et al."Improve efficiency and reduce bias of Cox regression models for two-stage randomization designs using auxiliary covariates".STATISTICS IN MEDICINE 36.11(2017):1683-1695.