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Optimal Linear Combinations of Multiple Diagnostic Biomarkers Based on Youden Index
Statistics in Medicine (2013)
  • Jingjing Yin, Georgia Southern University
  • Lili Tian, State University of New York at Buffalo

In practice, usually multiple biomarkers are measured on the same subject for disease diagnosis. Combining these biomarkers into a single score could improve diagnostic accuracy. Many researchers have addressed the problem of finding the optimal linear combination based on maximizing the area under ROC curve (AUC). Actually, such combined score might have less than optimal property at the diagnostic threshold. In this paper, we propose the idea of using Youden index as an objective function for searching the optimal linear combination. The combined score directly achieves the maximum overall correct classification rate at the diagnostic threshold corresponding to Youden index; in other words, it is the optimal linear combination score for making the disease diagnosis. We present both empirical and numerical searching methods for the optimal linear combination. We carry out extensive simulation study to investigate the performance of the proposed methods. Additionally, we empirically compare the optimal overall classification rates between the proposed combination based on Youden index and the traditional one based on AUC and demonstrate a significant gain in diagnostic accuracy for the proposed combination. In the end, we apply the proposed methods to a real data set.

  • ROC analysis,
  • Youden index,
  • Linear combination,
  • Diagnostic accuracy
Publication Date
Citation Information
Jingjing Yin and Lili Tian. "Optimal Linear Combinations of Multiple Diagnostic Biomarkers Based on Youden Index" Statistics in Medicine Vol. 33 Iss. 8 (2013)
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