Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews

BACKGROUND AND OBJECTIVES: Studies of diagnostic accuracy most often report pairsof sensitivity and specificity. We demonstrate the advantage of using bivariatemeta-regression models to analyze such data.METHODS: We discuss the methodology of both the summary Receiver OperatingCharacteristic (sROC) and the bivariate approach by reanalyzing the data of apublished meta-analysis.RESULTS: The sROC approach is the standard method for meta-analyzing diagnosticstudies reporting pairs of sensitivity and specificity. This method uses thediagnostic odds ratio as the main outcome measure, which removes the effect of a possible threshold but at the same time loses relevant clinical information abouttest 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 arandom effects approach. Explanatory variables can be added to the bivariatemodel 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 statisticalproperties of the bivariate model are sound and flexible.CONCLUSION: The bivariate model can be seen as an improvement and extension ofthe traditional sROC approach.