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Alternative Performance Measures for Prediction Models

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  • Yun-Chun Wu
  • Wen-Chung Lee

Abstract

As a performance measure for a prediction model, the area under the receiver operating characteristic curve (AUC) is insensitive to the addition of strong markers. A number of measures sensitive to performance change have recently been proposed; however, these relative-performance measures may lead to self-contradictory conclusions. This paper examines alternative performance measures for prediction models: the Lorenz curve-based Gini and Pietra indices, and a standardized version of the Brier score, the scaled Brier. Computer simulations are performed in order to study the sensitivity of these measures to performance change when a new marker is added to a baseline model. When the discrimination power of the added marker is concentrated in the gray zone of the baseline model, the AUC and the Gini show minimal performance improvements. The Pietra and the scaled Brier show more significant improvements in the same situation, comparatively. The Pietra and the scaled Brier indices are therefore recommended for prediction model performance measurement, in light of their ease of interpretation, clinical relevance and sensitivity to gray-zone resolving markers.

Suggested Citation

  • Yun-Chun Wu & Wen-Chung Lee, 2014. "Alternative Performance Measures for Prediction Models," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-6, March.
    • Handle: RePEc:plo:pone00:0091249
    DOI: 10.1371/journal.pone.0091249
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    Cited by:

    1. Edna Schechtman & Gideon Schechtman, 2019. "The relationship between Gini terminology and the ROC curve," METRON, Springer;Sapienza Università di Roma, vol. 77(3), pages 171-178, December.
    2. Bagas Suryo Bintoro & Yen-Chun Fan & Chia-Chi Chou & Kuo-Liong Chien & Chyi-Huey Bai, 2019. "Metabolic Unhealthiness Increases the Likelihood of Having Metabolic Syndrome Components in Normoweight Young Adults," IJERPH, MDPI, vol. 16(18), pages 1-14, September.
    3. Spada, Irene & Chiarello, Filippo & Barandoni, Simone & Ruggi, Gianluca & Martini, Antonella & Fantoni, Gualtiero, 2022. "Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing courses in Italy," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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