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Accommodating covariates in receiver operating characteristic analysis

accommodating covariates in receiver operating characteristic analysis-90

For factors that affect marker observations among controls, we present a method for covariate adjustment.

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For factors that affect marker observations among controls, we present a method for covariate adjustment. the ROC curve), we describe methods for modelling the ROC curve as a function of covariates.Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is com- monly summarized by using the receiver operating characteristic (ROC) curve.We describe three ways of using covariate information. (eds) Computational Science and Its Applications -- ICCSA 2015. In certain situations, the presence of related covariate, continuous or categorical, to the diagnostic variable can increase the discriminating power of the ROC curve [] on CRIB scale, using results of an intensive care unit of a Portuguese hospital. Springer, Cham In medical studies, the receiver operating characteristic (ROC) curve is a tool of extensive use to analyze the discrimination capability of a diagnostic variable.These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution. Keywords: roccurve; comproc; rocreg; receiver operating characteristic analysis; ROC; covariates; sensitivity; specificity (search for similar items in Econ Papers) Date: 2009 References: View references in Econ Papers View complete reference list from Cit Ec Citations View citations in Econ Papers (1) Track citations by RSS feed Downloads: (external link)

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For factors that affect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a function of covariates.

These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution. TY - JOURT1 - Accommodating covariates in receiver operating characteristic analysis AU - Janes, Holly AU - Longton, Gary AU - Pepe, Margaret S.

Biostatics and Biomathematics Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, M2-B500, Seattle, WA 98109 (M. Selecting one or more controls that are matched to each case on factors such as age, co-morbidities or study site improves study validity by eliminating potential biases due to differential characteristics of readings for cases versus controls.

F.); Department of Biostatistics, University of Washington, Health Sciences Building, Suite F-600, Seattle, WA 98195 (M. P); Departments of Critical Care and Emergency Medicine, Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Center, University of Pittsburgh, Pittsburgh, PA (CWS)Studies evaluating a new diagnostic imaging test may select control subjects without disease who are similar to case subjects with disease in regards to factors potentially related to the imaging result.

PY - 2009Y1 - 2009N2 - Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized by using the receiver operating characteristic (ROC) curve.