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Statistical Comparison of Two ROC-curve Estimates Obtained from Partially-paired Datasets

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  • Charles E. Metz
  • Benjamin A. Herman
  • Cheryl A. Roe

Abstract

The authors propose a new generalized method for ROC-curve fitting and statistical testing that allows researchers to utilize all of the data collected in an experimental comparison of two diagnostic modalities, even if some patients have not been studied with both modalities. Their new algorithm, ROCKIT, subsumes previous algorithms as special cases. It conducts all analyses available from previous ROC software and provides 95% confidence intervals for all estimates. R OCKIT was tested on more than half a million computer-simulated datasets of various sizes and configurations repre senting a range of population ROC curves. The algorithm successfully converged for more than 99.8% of all datasets studied. The type I error rates of the new algorithm's statistical test for differences in A z estimates were excellent for datasets typically en countered in practice, but diverged from alpha for datasets arising from some extreme situations. Key words. receiver operating characteristic (ROC) analysis, maximum-like lihood estimation; partially-paired data; missing data. (Med Decis Making 1998;18: 110-121)

Suggested Citation

  • Charles E. Metz & Benjamin A. Herman & Cheryl A. Roe, 1998. "Statistical Comparison of Two ROC-curve Estimates Obtained from Partially-paired Datasets," Medical Decision Making, , vol. 18(1), pages 110-121, January.
    • Handle: RePEc:sae:medema:v:18:y:1998:i:1:p:110-121
    DOI: 10.1177/0272989X9801800118
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    Cited by:

    1. Simon Sosvilla-Rivero & Pedro Rodriguez, 2010. "Linkages in international stock markets: evidence from a classification procedure," Applied Economics, Taylor & Francis Journals, vol. 42(16), pages 2081-2089.
    2. Frederico Z. Poleto & Julio M. Singer & Carlos Daniel Paulino, 2011. "Comparing diagnostic tests with missing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(6), pages 1207-1222, April.
    3. Kong, Guilan & Xu, Dong-Ling & Body, Richard & Yang, Jian-Bo & Mackway-Jones, Kevin & Carley, Simon, 2012. "A belief rule-based decision support system for clinical risk assessment of cardiac chest pain," European Journal of Operational Research, Elsevier, vol. 219(3), pages 564-573.
    4. Zhang, Biao, 2006. "A semiparametric hypothesis testing procedure for the ROC curve area under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1855-1876, April.

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