Accommodating covariates in roc analysis
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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 ROC analysis of single test data has been extensively investigated, see Qin and Zhou (2006), Qin et al.
(2011), and Hsieh and Turnbull (1996), since the seminal work by Dorfman and Alf (1969).
(2012) are the existing methods to estimate ROC curves from this type of clustered data.
Li and Zhou (2008) discussed a nonparametric method for estimating the ROC curve for clustered data. (2012) extended Li and Zhou (2008)’s non-parametric methods to allow the simultaneous comparison among multiple tests.
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.
To the best of our knowledge, Li and Zhou (2008) and Tang et al.We also derive the asymptotic properties of the proposed methods.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.Although Obuchowski (1997) also considered clustered diagnostic test data, her methods dealt with the area under the ROC curve, which can be considered as a special case of Li and Zhou’s method.
These nonparametric methods generated rough ROC curve estimators, and did not incorporate smoothing techniques in estimation.
Furthermore, various discrete covariates may have effects on ROC curve analysis (Zhou et al., 2011).
Was generalized based on the Lehmann assumption also known as the proportional hazards specification. This model accommodates a variety of research questions such as covariate adjustments. By applying this method to three-class ROC analysis, simple analytical forms of ROC surface and volume under the surface.… continue reading »
Jul 2, 2015. ROC curves and in particular AUC are widely used in medical studies to examine the effectiveness of markers used. For some useful references in analysis of ROC curve when covariates are present, one may refer to Janes et al. Accommodating covariates in receiver operating characteristic analysis.… continue reading »
Jan 22, 2008. Alternatively pointwise comparisons between ROC curves or inverse ROC curves can be made. Options to adjust these analyses for covariates, and to perform ROC regression are described in a companion article. We use a unified framework by representing the ROC curve as the distribution of the marker.… continue reading »
ROC curves are a popular method for displaying sensitivity and specificity of a continuous. as ROCt. A typical complexity with survival data is that observations may be censored. Two ROC curve estimators are proposed that can accommodate censored data. a covariate X, and a binary disease variable Di by plotting.… continue reading »
Viously been used for evaluating goodness of fit for logistic regression models and for constructing ROC curves based on raw marker measures only Qin and. Lawless 1994, Qin and Zhang 1997, 2003. Here we extend it to accommodate additional covariates using standardized marker values that are estimated. Let FU.… continue reading »
Jun 23, 2017. In Receiver Operating Characteristic ROC curve analysis, many factors such as the study subject's characteristics or operating conditions of a medical test may affect the diagnostic accuracy of the test. ROC regression models are introduced to accommodate effects of the covariates. If many covariates are.… continue reading »