Bivariate Analysis of Sensitivity and Specificity Produces Informative Summary Measures in Diagnostic Reviews

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.