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Accommodating covariates in receiver operating characteristic analysis

Author

Listed:
  • Holly Janes

    (Fred Hutchinson Cancer Research Center)

  • Gary Longton

    (Fred Hutchinson Cancer Research Center)

  • Margaret S. Pepe

    (Fred Hutchinson Cancer Research Center)

Abstract

Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is com- monly summarized by using the receiver operating characteristic (ROC) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three ways of using covariate information. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a function of covariates. Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and we ask how much discriminatory accuracy improves (in incremental value) with the addition of the marker to the covariates. These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution. Copyright 2009 by StataCorp LP.

Suggested Citation

  • Holly Janes & Gary Longton & Margaret S. Pepe, 2009. "Accommodating covariates in receiver operating characteristic analysis," Stata Journal, StataCorp LP, vol. 9(1), pages 17-39, March.
    • Handle: RePEc:tsj:stataj:v:9:y:2009:i:1:p:17-39
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    References listed on IDEAS

    as
    1. Margaret Sullivan Pepe, 2000. "An Interpretation for the ROC Curve and Inference Using GLM Procedures," Biometrics, The International Biometric Society, vol. 56(2), pages 352-359, June.
    2. P. J. Heagerty & M. S. Pepe, 1999. "Semiparametric estimation of regression quantiles with application to standardizing weight for height and age in US children," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 48(4), pages 533-551.
    3. Lori E. Dodd & Margaret S. Pepe, 2003. "Partial AUC Estimation and Regression," Biometrics, The International Biometric Society, vol. 59(3), pages 614-623, September.
    4. Margaret S. Pepe & Gary Longton & Holly Janes, 2009. "Estimation and comparison of receiver operating characteristic curves," Stata Journal, StataCorp LP, vol. 9(1), pages 1-16, March.
    5. Edward C. Norton & Hua Wang & Chunrong Ai, 2004. "Computing interaction effects and standard errors in logit and probit models," Stata Journal, StataCorp LP, vol. 4(2), pages 154-167, June.
    6. Martin W. McIntosh & Margaret Sullivan Pepe, 2002. "Combining Several Screening Tests: Optimality of the Risk Score," Biometrics, The International Biometric Society, vol. 58(3), pages 657-664, September.
    7. Margaret Sullivan Pepe & Tianxi Cai, 2004. "The Analysis of Placement Values for Evaluating Discriminatory Measures," Biometrics, The International Biometric Society, vol. 60(2), pages 528-535, June.
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    Cited by:

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    2. Thewissen, James & Shrestha, Prabal & Torsin, Wouter & Pastwa, Anna M., 2022. "Unpacking the black box of ICO white papers: A topic modeling approach," Journal of Corporate Finance, Elsevier, vol. 75(C).
    3. Bandyopadhyay, Tathagata & Sumanta Adhya & Guha, Apratim, 2015. "ROC Curve Analysis for Randomly Selected Patients," IIMA Working Papers WP2015-07-02, Indian Institute of Management Ahmedabad, Research and Publication Department.

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