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Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve

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  • Holly Janes
  • Margaret S. Pepe

Abstract

Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer. Copyright 2009, Oxford University Press.

Suggested Citation

  • Holly Janes & Margaret S. Pepe, 2009. "Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve," Biometrika, Biometrika Trust, vol. 96(2), pages 371-382.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:2:p:371-382
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    File URL: http://hdl.handle.net/10.1093/biomet/asp002
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    Citations

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    Cited by:

    1. Akiko Kada & Zhihong Cai & Manabu Kuroki, 2013. "Medical diagnostic test based on the potential test result approach: bounds and identification," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(8), pages 1659-1672, August.
    2. Morkoetter, Stefan & Stebler, Roman & Westerfeld, Simone, 2017. "Competition in the credit rating Industry: Benefits for investors and issuers," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 235-257.
    3. Danping Liu & Xiao-Hua Zhou, 2013. "Covariate Adjustment in Estimating the Area Under ROC Curve with Partially Missing Gold Standard," Biometrics, The International Biometric Society, vol. 69(1), pages 91-100, March.
    4. Mark R. O’Donovan & Nicola Cornally & Rónán O’Caoimh, 2023. "Validation of a Harmonised, Three-Item Cognitive Screening Instrument for the Survey of Health, Ageing and Retirement in Europe (SHARE-Cog)," IJERPH, MDPI, vol. 20(19), pages 1-14, September.
    5. Ziyi Li & Yijian Huang & Dattatraya Patil & Martin G. Sanda, 2023. "Covariate adjustment in continuous biomarker assessment," Biometrics, The International Biometric Society, vol. 79(1), pages 39-48, March.
    6. Wei Jiang & Ling Chen & Matthew J. Girgenti & Hongyu Zhao, 2024. "Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    7. Pardo-Fernandez, Juan Carlos & Rodriguez-alvarez, Maria Xose & Van Keilegom, Ingrid, 2013. "A review on ROC curves in the presence of covariates," LIDAM Discussion Papers ISBA 2013050, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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