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Using relative utility curves to evaluate risk prediction

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  • Stuart G. Baker
  • Nancy R. Cook
  • Andrew Vickers
  • Barnett S. Kramer

Abstract

Summary. Because many medical decisions are based on risk prediction models that are constructed from medical history and results of tests, the evaluation of these prediction models is important. This paper makes five contributions to this evaluation: the relative utility curve which gauges the potential for better prediction in terms of utilities, without the need for a reference level for one utility, while providing a sensitivity analysis for misspecification of utilities, the relevant region, which is the set of values of prediction performance that are consistent with the recommended treatment status in the absence of prediction, the test threshold, which is the minimum number of tests that would be traded for a true positive prediction in order for the expected utility to be non‐negative, the evaluation of two‐stage predictions that reduce test costs and connections between various measures of performance of prediction. An application involving the risk of cardiovascular disease is discussed.

Suggested Citation

  • Stuart G. Baker & Nancy R. Cook & Andrew Vickers & Barnett S. Kramer, 2009. "Using relative utility curves to evaluate risk prediction," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(4), pages 729-748, October.
    • Handle: RePEc:bla:jorssa:v:172:y:2009:i:4:p:729-748
    DOI: 10.1111/j.1467-985X.2009.00592.x
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    References listed on IDEAS

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    1. William M. Briggs & Russell Zaretzki, 2008. "The Skill Plot: A Graphical Technique for Evaluating Continuous Diagnostic Tests," Biometrics, The International Biometric Society, vol. 64(1), pages 250-256, March.
    2. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    3. Ying Huang & Margaret Sullivan Pepe & Ziding Feng, 2007. "Evaluating the Predictiveness of a Continuous Marker," Biometrics, The International Biometric Society, vol. 63(4), pages 1181-1188, December.
    4. Patrick J. Heagerty & Thomas Lumley & Margaret S. Pepe, 2000. "Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker," Biometrics, The International Biometric Society, vol. 56(2), pages 337-344, June.
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    Cited by:

    1. Hormuzd A. Katki & Ionut Bebu, 2021. "A simple framework to identify optimal cost‐effective risk thresholds for a single screen: Comparison to Decision Curve Analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 887-903, July.
    2. Ben Van Calster & Andrew J. Vickers, 2015. "Calibration of Risk Prediction Models," Medical Decision Making, , vol. 35(2), pages 162-169, February.
    3. Baker Stuart G. & Van Calster Ben & Steyerberg Ewout W., 2012. "Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-37, March.
    4. Ben Van Calster & Andrew J. Vickers & Michael J. Pencina & Stuart G. Baker & Dirk Timmerman & Ewout W. Steyerberg, 2013. "Evaluation of Markers and Risk Prediction Models," Medical Decision Making, , vol. 33(4), pages 490-501, May.
    5. Stuart G. Baker, 2024. "Evaluating Risk Prediction with Data Collection Costs: Novel Estimation of Test Tradeoff Curves," Medical Decision Making, , vol. 44(1), pages 53-63, January.
    6. Ben Van Calster & Ewout W. Steyerberg & Ralph B. D’Agostino Sr & Michael J. Pencina, 2014. "Sensitivity and Specificity Can Change in Opposite Directions When New Predictive Markers Are Added to Risk Models," Medical Decision Making, , vol. 34(4), pages 513-522, May.
    7. Mei-Cheng Wang & Shanshan Li, 2012. "Bivariate Marker Measurements and ROC Analysis," Biometrics, The International Biometric Society, vol. 68(4), pages 1207-1218, December.
    8. Tracey L. Marsh & Holly Janes & Margaret S. Pepe, 2020. "Statistical inference for net benefit measures in biomarker validation studies," Biometrics, The International Biometric Society, vol. 76(3), pages 843-852, September.
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    10. Holly Janes & Margaret S. Pepe & Ying Huang, 2014. "A Framework for Evaluating Markers Used to Select Patient Treatment," Medical Decision Making, , vol. 34(2), pages 159-167, February.
    11. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2019. "A sparse random projection-based test for overall qualitative treatment effects," LSE Research Online Documents on Economics 102107, London School of Economics and Political Science, LSE Library.

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