Improve efficiency and reduce bias of Cox regression models for two-stage randomization designs using auxiliary covariates
文献类型：期刊论文
| 作者 | Yang, Xue1,2; Zhou, Yong1,3
|
| 刊名 | STATISTICS IN MEDICINE
 |
| 出版日期 | 2017-05-20 |
| 卷号 | 36期号:11页码:1683-1695 |
| 关键词 | two-stage randomization design inverse probability weighting Cox regression covariate adjustment semiparametric theory projection theorem |
| ISSN号 | 0277-6715 |
| DOI | 10.1002/sim.7252 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000400595100001 |
| 出版者 | WILEY |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/26004]  |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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