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Objective: Markers which are used for classification into two groups, such as patient / healthy, benign/malignant or prediction of optimal cut off value for diagnostic test and evaluating the performance of diagnostic tests is evaluated by Receiver Operating Characteristic (ROC) curve in the diagnostic test researches. In classification accuracy research, some variables such as gender and age, commonly is not similar in groups. In these cases, covariates should be considered to estimate in the area under ROC and covariate adjustment for ROC should be performed. This study aims to introduce methods in the literature for the effect of covariate adjustment and to present an application with sample from the health field. Material and Methods: In the study, we introduced methods used in the literature for covariate adjustment and prediction of the area under ROC curves as well as an application with data from the field of urology. In this study, 105 PSA (prostate specific antigen) measurements were taken in order to examine the covariate effect for the age variable and to assess the diagnostic performance of PSA measurements with regard to pathologic methods. Results: Covariate effect were found statistically significant with 0.733 parameter estimation of the age in ROC curves analysis with PSA data (p<0.001). According to the methods (Non-parametric (empirical), non-parametric (normal), semi-parametric (empirical), parametric (normal)) that estimates of the area under ROC curves which is obtained without covariate effect were found 0.708, 0.629, 0.709 and 0.628, respectively, by using PSA measurements. Area under the curve that obtained by covariate adjustment were significantly lower as compared to the traditional ROC with estimation 0.580, 0.577, 0.582 and 0.579. Conclusion: Area under the ROC curves should be estimated with adjustment according to the covariates that could affect the markers value of diagnostic tests performed in concert with matching designs in research.

Covariate Adjustment in Receiver Operating Characteristic Curve Analysis for PSA in Diagnosis of Prostate Cancer