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Bivariate Random Effects Meta-Analysis of ROC Curves

Author

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  • L.R. Arends

    (Department of Epidemiology & Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands, Institute of Psychology, Erasmus University Rotterdam, Rotterdam, The Netherlands, l.arends@erasmusmc.nl)

  • T.H. Hamza

    (Department of Epidemiology & Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands)

  • J.C. van Houwelingen

    (Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands)

  • M.H. Heijenbrok-Kal

    (Department of Epidemiology & Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands, Department of Radiology, Erasmus Medical Center, Rotterdam, Netherlands)

  • M.G.M. Hunink

    (Department of Epidemiology & Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands, Department of Radiology, Erasmus Medical Center, Rotterdam, Netherlands, Department of Health Policy & Management, Harvard School of Public Health, Boston, Massachusetts)

  • T. Stijnen

    (Department of Epidemiology & Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands)

Abstract

Meta-analysis of receiver operating characteristic (ROC)-curve data is often done with fixed-effects models, which suffer many shortcomings. Some random-effects models have been proposed to execute a meta-analysis of ROC-curve data, but these models are not often used in practice. Straightforward modeling techniques for multivariate random-effects meta-analysis of ROC-curve data are needed. The 1st aim of this article is to present a practical method that addresses the drawbacks of the fixedeffects summary ROC (SROC) method of Littenberg and Moses. Sensitivities and specificities are analyzed simultaneously using a bivariate random-effects model. The 2nd aim is to show that other SROC curves can also be derived from the bivariate model through different characterizations of the estimated bivariate normal distribution. Thereby the authors show that the bivariate random-effects approach not only extends the SROC approach but also provides a unifying framework for other approaches. The authors bring the statistical meta-analysis of ROC-curve data back into a framework of relatively standard multivariate meta-analysis with random effects. The analyses were carried out using the software package SAS (Proc NLMIXED).

Suggested Citation

  • L.R. Arends & T.H. Hamza & J.C. van Houwelingen & M.H. Heijenbrok-Kal & M.G.M. Hunink & T. Stijnen, 2008. "Bivariate Random Effects Meta-Analysis of ROC Curves," Medical Decision Making, , vol. 28(5), pages 621-638, September.
    • Handle: RePEc:sae:medema:v:28:y:2008:i:5:p:621-638
    DOI: 10.1177/0272989X08319957
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    References listed on IDEAS

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    1. Roger Harbord & Julian Higgins, 2004. "METAREG: Stata module to perform meta-analysis regression," Statistical Software Components S446201, Boston College Department of Economics, revised 05 Jan 2009.
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    Cited by:

    1. Ruili Wei & Chaonan Wang & Fangping He & Lirong Hong & Jie Zhang & Wangxiao Bao & Fangxia Meng & Benyan Luo, 2019. "Prediction of poor outcome after hypoxic-ischemic brain injury by diffusion-weighted imaging: A systematic review and meta-analysis," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-16, December.
    2. Philipp Doebler & Heinz Holling, 2015. "Meta-analysis of Diagnostic Accuracy and ROC Curves with Covariate Adjusted Semiparametric Mixtures," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 1084-1104, December.
    3. Aristidis K. Nikoloulopoulos, 2018. "On composite likelihood in bivariate meta-analysis of diagnostic test accuracy studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(2), pages 211-227, April.
    4. Alexander J. Sutton & Nicola J. Cooper & Steve Goodacre & Matthew Stevenson, 2008. "Integration of Meta-analysis and Economic Decision Modeling for Evaluating Diagnostic Tests," Medical Decision Making, , vol. 28(5), pages 650-667, September.
    5. Jie Chen & Mingpeng Wu & Rongbo Liu & Siyi Li & Ronghui Gao & Bin Song, 2015. "Preoperative Evaluation of the Histological Grade of Hepatocellular Carcinoma with Diffusion-Weighted Imaging: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-16, February.

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