Accommodating covariates roc analysis single parent dating east glastonbury connecticut
(2011), and Hsieh and Turnbull (1996), since the seminal work by Dorfman and Alf (1969).
Statistical tests are often complicated when used in diagnostic biomarker studies where two or more different diagnostic biomarkers are simultaneously measured on normal and abnormal locations.
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 non-diseased group when test results from tests are continuous or ordinal.
The methods can generate smooth ROC curves which satisfy the inherent continuous property of the true underlying curve.
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.
ROC regression models are introduced to accommodate effects of the covariates.
The proposed method is illustrated through simulation studies and a real data example.
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.
In addition, simultaneous confidence bands can be obtained using the ROC parameter estimators and their estimated variances from the proposed methods to visualize the uncertainty of the estimated ROC curves.
Furthermore, various discrete covariates may have effects on ROC curve analysis (Zhou et al., 2011).
To the best of our knowledge, Li and Zhou (2008) and Tang et al.