Accommodating covariates in roc analysis Free adult 2way cams


31-Jul-2020 17:56

Export reference: Persistent link: https://Econ Papers.repec.org/Re PEc:tsj:stataj:v:9:y:2009:i:1:p:17-39 Ordering information: This journal article can be ordered from Statistics for this article Stata Journal is currently edited by H.Adjusting the generalized ROC curve for covariates.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.In the present work, we propose to analyze the discriminative power of CRIB scale, using ROC regression analysis with GLM (Generalized Linear Models), in the classification of babies with and without the presence of covariates (newborn gender and mothers age).It was found that adjusted AUC value was higher than unadjusted AUC value.The Statistical Evaluation of Medical Tests for Classification and Prediction.article=st0155 (text/html) Related works: This item may be available elsewhere in Econ Papers: Search for items with the same title.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 using the receiver operating characteristic (ROC) curve.

accommodating covariates in roc analysis-23

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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.We describe three ways of using covariate information.



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