CSpace  > 应用数学研究所
Improve efficiency and reduce bias of Cox regression models for two-stage randomization designs using auxiliary covariates
Yang, Xue1,2; Zhou, Yong1,3
2017-05-20
Source PublicationSTATISTICS IN MEDICINE
ISSN0277-6715
Volume36Issue:11Pages:1683-1695
AbstractTwo-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.
Keywordtwo-stage randomization design inverse probability weighting Cox regression covariate adjustment semiparametric theory projection theorem
DOI10.1002/sim.7252
Language英语
Funding ProjectNational 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 Research AreaMathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Research & Experimental Medicine ; Mathematics
WOS SubjectMathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Medicine, Research & Experimental ; Statistics & Probability
WOS IDWOS:000400595100001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/26005
Collection应用数学研究所
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yang, Xue]'s Articles
[Zhou, Yong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yang, Xue]'s Articles
[Zhou, Yong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yang, Xue]'s Articles
[Zhou, Yong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.