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

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  • Sudesh Pundir
  • R. Amala

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.

Suggested Citation

  • Sudesh Pundir & R. Amala, 2015. "Detecting diagnostic accuracy of two biomarkers through a bivariate log-normal ROC curve," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2671-2685, December.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:12:p:2671-2685
    DOI: 10.1080/02664763.2015.1046823
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    Cited by:

    1. Nurulkamal Masseran, 2021. "Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach," IJERPH, MDPI, vol. 18(16), pages 1-18, August.

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