中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 数学与系统科学研究院
  3. 中国科学院数学与系统科学研究院
  4. 系统科学研究所
Least squares regression methods for clustered ROC data with discrete covariates

文献类型：期刊论文

作者Tang, Liansheng Larry1,2; Zhang, Wei3; Li, Qizhai3; Ye, Xuan1; Chan, Leighton2
刊名BIOMETRICAL JOURNAL
出版日期2016-07-01
卷号58期号:4页码:747-765
关键词Biomarker Clustered data Empirical function ROC
ISSN号0323-3847
DOI10.1002/bimj.201500099
英文摘要The receiver operating characteristic (ROC) curve is a popular tool to evaluate and compare the accuracy of diagnostic tests to distinguish the diseased group from the nondiseased group when test results from tests are continuous or ordinal. A complicated data setting occurs when multiple tests are measured on abnormal and normal locations from the same subject and the measurements are clustered within the subject. Although least squares regression methods can be used for the estimation of ROC curve from correlated data, how to develop the least squares methods to estimate the ROC curve from the clustered data has not been studied. Also, the statistical properties of the least squares methods under the clustering setting are unknown. In this article, we develop the least squares ROC methods to allow the baseline and link functions to differ, and more importantly, to accommodate clustered data with discrete covariates. The methods can generate smooth ROC curves that satisfy the inherent continuous property of the true underlying curve. The least squares methods are shown to be more efficient than the existing nonparametric ROC methods under appropriate model assumptions in simulation studies. We apply the methods to a real example in the detection of glaucomatous deterioration. We also derive the asymptotic properties of the proposed methods.
资助项目Intramural Research Program of the National Institutes of Health ; U.S. Social Security Administration ; National Natural Science of China[11371353] ; National Natural Science of China[61134013] ; Strategic Priority Research Program of the Chinese Academy of Sciences
WOS研究方向Mathematical & Computational Biology ; Mathematics
语种英语
WOS记录号WOS:000379929300002
出版者WILEY-BLACKWELL
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/23207]  
专题系统科学研究所
通讯作者Tang, Liansheng Larry
作者单位1.George Mason Univ, Dept Stat, Fairfax, VA 22030 USA
2.NIH, Epidemiol & Biostat, Ctr Clin, Rockville, MD 20814 USA
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Tang, Liansheng Larry,Zhang, Wei,Li, Qizhai,et al. Least squares regression methods for clustered ROC data with discrete covariates[J]. BIOMETRICAL JOURNAL,2016,58(4):747-765.
APA Tang, Liansheng Larry,Zhang, Wei,Li, Qizhai,Ye, Xuan,&Chan, Leighton.(2016).Least squares regression methods for clustered ROC data with discrete covariates.BIOMETRICAL JOURNAL,58(4),747-765.
MLA Tang, Liansheng Larry,et al."Least squares regression methods for clustered ROC data with discrete covariates".BIOMETRICAL JOURNAL 58.4(2016):747-765.

入库方式： OAI收割

来源：数学与系统科学研究院

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。