Detecting diagnostic accuracy of two biomarkers through a bivariate log-normal ROC curve

Abstract

In biomedical research, two or more biomarkers may be available for diagnosis of a particular disease. Selecting one single biomarker which ideally discriminate a diseased group from a healthy group is confront in a diagnostic process. Frequently, most of the people use the accuracy measure, area under the receiver operating characteristic (ROC) curve to choose the best diagnostic marker among the available markers for diagnosis. Some authors have tried to combine the multiple markers by an optimal linear combination to increase the discriminatory power. In this paper, we propose an alternative method that combines two continuous biomarkers by direct bivariate modeling of the ROC curve under log-normality assumption. The proposed method is applied to simulated data set and prostate cancer diagnostic biomarker data set.

Keywords:

• area under the ROC curve
• bivariate biomarker
• log-normal distribution
• Monte Carlo simulation and receiver operating characteristic curve

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The authors wish to express their gratitude to the University Grants Commission for the financial assistance through UGC Major Research Project and to the Pondicherry University for the financial assistance through University Research Fellowship to carry out this research.