Least squares regression methods for clustered ROC data with discrete covariates

doi:10.1002/bimj.201500099

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 系统科学研究所

	Least squares regression methods for clustered ROC data with discrete covariates
	Tang, Liansheng Larry 1,2; Zhang, Wei 3; Li, Qizhai3 ; Ye, Xuan 1; Chan, Leighton 2
刊名	BIOMETRICAL JOURNAL
	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
DOI	10.1002/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
出版者	WILEY-BLACKWELL
WOS记录号	WOS:000379929300002
内容类型	期刊论文
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.