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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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    1. J. P. Royston, 1983. "Some Techniques for Assessing Multivarate Normality Based on the Shapiro‐Wilk W," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(2), pages 121-133, June.
    2. Henze, Norbert & Wagner, Thorsten, 1997. "A New Approach to the BHEP Tests for Multivariate Normality," Journal of Multivariate Analysis, Elsevier, vol. 62(1), pages 1-23, July.
    3. Shuangge Ma & Jian Huang, 2007. "Combining Multiple Markers for Classification Using ROC," Biometrics, The International Biometric Society, vol. 63(3), pages 751-757, September.
    4. Margaret Sullivan Pepe, 2000. "An Interpretation for the ROC Curve and Inference Using GLM Procedures," Biometrics, The International Biometric Society, vol. 56(2), pages 352-359, June.
    5. Robert J. Richards & James K. Hammitt & Joel Tsevat, 1996. "Finding the Optimal Multiple-test Strategy Using a Method Analogous to Logistic Regression," Medical Decision Making, , vol. 16(4), pages 367-375, October.
    6. Ruth Etzioni & Margaret Pepe & Gary Longton & Chengcheng Hu & Gary Goodman, 1999. "Incorporating the Time Dimension in Receiver Operating Characteristic Curves: A Case Study of Prostate Cancer," Medical Decision Making, , vol. 19(3), pages 242-251, August.
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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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