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An Integrated Bayesian Nonparametric Approach for Stochastic and Variability Orders in ROC Curve Estimation: An Application to Endometriosis Diagnosis

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  • Beom Seuk Hwang
  • Zhen Chen

Abstract

In estimating ROC curves of multiple tests, some a priori constraints may exist, either between the healthy and diseased populations within a test or between tests within a population. In this article, we proposed an integrated modeling approach for ROC curves that jointly accounts for stochastic and variability orders. The stochastic order constrains the distributional centers of the diseased and healthy populations within a test, while the variability order constrains the distributional spreads of the tests within each of the populations. Under a Bayesian nonparametric framework, we used features of the Dirichlet process mixture to incorporate these order constraints in a natural way. We applied the proposed approach to data from the Physician Reliability Study that investigated the accuracy of diagnosing endometriosis using different clinical information. To address the issue of no gold standard in the real data, we used a sensitivity analysis approach that exploited diagnosis from a panel of experts. To demonstrate the performance of the methodology, we conducted simulation studies with varying sample sizes, distributional assumptions, and order constraints. Supplementary materials for this article are available online.

Suggested Citation

  • Beom Seuk Hwang & Zhen Chen, 2015. "An Integrated Bayesian Nonparametric Approach for Stochastic and Variability Orders in ROC Curve Estimation: An Application to Endometriosis Diagnosis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 923-934, September.
  • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:511:p:923-934
    DOI: 10.1080/01621459.2015.1023806
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    References listed on IDEAS

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    1. Bo Zhang & Zhen Chen & Paul S. Albert, 2012. "Estimating Diagnostic Accuracy of Raters Without a Gold Standard by Exploiting a Group of Experts," Biometrics, The International Biometric Society, vol. 68(4), pages 1294-1302, December.
    2. Fengchun Peng & W.Jack Hall, 1996. "Bayesian Analysis of ROC Curves Using Markov-chain Monte Carlo Methods," Medical Decision Making, , vol. 16(4), pages 404-411, October.
    3. Timothy E. Hanson & Athanasios Kottas & Adam J. Branscum, 2008. "Modelling stochastic order in the analysis of receiver operating characteristic data: Bayesian non‐parametric approaches," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(2), pages 207-225, April.
    4. 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.
    5. David B. Dunson & Shyamal D. Peddada, 2008. "Bayesian nonparametric inference on stochastic ordering," Biometrika, Biometrika Trust, vol. 95(4), pages 859-874.
    6. Peter D. Hoff, 2003. "Bayesian methods for partial stochastic orderings," Biometrika, Biometrika Trust, vol. 90(2), pages 303-317, June.
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    2. Wei Zhang & Larry L. Tang & Qizhai Li & Aiyi Liu & Mei‐Ling Ting Lee, 2020. "Order‐restricted inference for clustered ROC data with application to fingerprint matching accuracy," Biometrics, The International Biometric Society, vol. 76(3), pages 863-873, September.
    3. 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.

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