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A new integrated discrimination improvement index via odds

Author

Listed:
  • Kenichi Hayashi

    (Keio University)

  • Shinto Eguchi

    (The Institute of Mathematical Science)

Abstract

Consider adding new covariates to an established binary regression model to improve prediction performance. Although difference in the area under the ROC curve (delta AUC) is typically used to evaluate the degree of improvement in such situations, its power is not high due to being a rank-based statistic. As an alternative to delta AUC, integrated discrimination improvement (IDI) has been proposed by Pencina et al. (2008). However, several papers have pointed out that IDI erroneously detects meaningless improvement. In the present study, we propose a novel index for prediction improvement having Fisher consistency, implying that it overcomes the problems in both delta AUC and IDI. Furthermore, our proposed index also has an advantage that the index we proposed in our previous study (Hayashi and Eguchi 2019) lacked: it does not require any hyperparameters or complicated transformations that would make interpretation difficult.

Suggested Citation

  • Kenichi Hayashi & Shinto Eguchi, 2024. "A new integrated discrimination improvement index via odds," Statistical Papers, Springer, vol. 65(8), pages 4971-4990, October.
    • Handle: RePEc:spr:stpapr:v:65:y:2024:i:8:d:10.1007_s00362-024-01585-7
    DOI: 10.1007/s00362-024-01585-7
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    References listed on IDEAS

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    1. Margaret Pepe & Holly Janes & Gary Longton & Wendy Leisenring & Polly Newcomb, 2004. "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker," UW Biostatistics Working Paper Series 1035, Berkeley Electronic Press.
    2. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    3. Stefanski L. A. & Boos D. D., 2002. "The Calculus of M-Estimation," The American Statistician, American Statistical Association, vol. 56, pages 29-38, February.
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