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    Accommodating covariates in receiver operating characteristic analysis

    The Kendall tau test of dependency for PAUC and a judgmental index of forecast method complexity provide further confirming evidence.

    We also found that decision-rule combination forecasts using three top methods generally perform better than the component methods, although not statistically so.

    An interpretation for the ROC curve and inference using GLM procedures.

    The Statistical Evaluation of Medical Tests for Classification and Prediction.

    Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting.

    Three approaches to regression analysis of receiver operating characteristic curves for continuous test results.

    PSA and age values of 125 patients who were examined prostate biopsy with pre-diagnosis of prostate cancer in Gaziosmanpasa University Faculty of Medicine Department of Urology at the years of 2005 to 2007. Distribution-free ROC analysis using binary regression techniques.

    Adjusting the generalized ROC curve for covariates.

    Ordinal regression methodology for ROC curves derived from correlated data.

    The AUC was significantly larger when the biomarker indicators in disease group were higher.

    In addition, if the correlation between the covariate and biomarker is high in disease group and if AUC is approximately 0.75, then there is significant difference between adjusted AUC and AUC.

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