Bivariate Random Effects Meta-Analysis of ROC Curves

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 mo...

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Bibliographic Details
Published in:Medical decision making 2008-09, Vol.28 (5), p.621-638
Main Authors: Arends, L.R., Hamza, T.H., van Houwelingen, J.C., Heijenbrok-Kal, M.H., Hunink, M.G.M., Stijnen, T.
Format: Article
Language:English
Subjects:
Analysis
Data Interpretation, Statistical
Decision-making
Diagnostic Tests, Routine - standards
Humans
Medical tests
Meta-Analysis as Topic
Methods
Models, Statistical
Psychological aspects
ROC Curve
Signal detection (Psychology)
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Summary: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).
ISSN:0272-989X
1552-681X
DOI:10.1177/0272989X08319957