Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews
- PMID: 16168343
- DOI: 10.1016/j.jclinepi.2005.02.022
Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews
Abstract
Background and objectives: Studies of diagnostic accuracy most often report pairs of sensitivity and specificity. We demonstrate the advantage of using bivariate meta-regression models to analyze such data.
Methods: We discuss the methodology of both the summary Receiver Operating Characteristic (sROC) and the bivariate approach by reanalyzing the data of a published meta-analysis.
Results: The sROC approach is the standard method for meta-analyzing diagnostic studies reporting pairs of sensitivity and specificity. This method uses the diagnostic odds ratio as the main outcome measure, which removes the effect of a possible threshold but at the same time loses relevant clinical information about test performance. The bivariate approach preserves the two-dimensional nature of the original data. Pairs of sensitivity and specificity are jointly analyzed, incorporating any correlation that might exist between these two measures using a random effects approach. Explanatory variables can be added to the bivariate model and lead to separate effects on sensitivity and specificity, rather than a net effect on the odds ratio scale as in the sROC approach. The statistical properties of the bivariate model are sound and flexible.
Conclusion: The bivariate model can be seen as an improvement and extension of the traditional sROC approach.
Comment in
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Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach.J Clin Epidemiol. 2006 Dec;59(12):1331-2; author reply 1332-3. doi: 10.1016/j.jclinepi.2006.06.011. Epub 2006 Sep 28. J Clin Epidemiol. 2006. PMID: 17098577 No abstract available.
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