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Receiver Operator characteristic (ROC) Analysis without Truth

Author

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  • R. Mark Henkelman
  • Ian Kay
  • Michael J. Bronskill

Abstract

Receiver operator characteristic (ROC) analysis, the preferred method of evaluating diag nostic imaging tests, requires an independent assessment of the true state of disease, which can be difficult to obtain and is often of questionable accuracy. A new method of analysis is described which does not require independent truth data and which can be used when several accurate tests are being compared. This method uses correlative information to estimate the underlying model of multivariate normal distributions of disease-positive and disease-negative patients. The method is shown to give results equivalent to conventional ROC analysis in a comparison of computed tomography, radionuclide scintigraphy, and magnetic resonance imaging for liver metastasis. When independent truth is available, the method can be extended to incorporate truth data or to evaluate the consistency of the truth data with the imaging data. Key words : receiver operator characteristics; diagnostic evalu ation. (Med Decis Making 1990;10:24-29)

Suggested Citation

  • R. Mark Henkelman & Ian Kay & Michael J. Bronskill, 1990. "Receiver Operator characteristic (ROC) Analysis without Truth," Medical Decision Making, , vol. 10(1), pages 24-29, February.
    • Handle: RePEc:sae:medema:v:10:y:1990:i:1:p:24-29
    DOI: 10.1177/0272989X9001000105
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    Cited by:

    1. Chinyereugo M Umemneku Chikere & Kevin Wilson & Sara Graziadio & Luke Vale & A Joy Allen, 2019. "Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard – An update," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
    2. Xiao-Hua Zhou & Pete Castelluccio & Chuan Zhou, 2004. "Non-Parametric Estimation of ROC Curves in the Absence of a Gold Standard," UW Biostatistics Working Paper Series 1064, Berkeley Electronic Press.
    3. Wang, Zheyu & Sebestyen, Krisztian & Monsell, Sarah E., 2017. "Model-based clustering for assessing the prognostic value of imaging biomarkers and mixed type tests," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 125-135.

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