Bayesian bivariate meta-analysis of sensitivity and specificity: summary of quantitative findings in 50 meta-analyses

Meta-analyses of diagnostic test accuracy are important elements in evidence-based medicine. However, currently there is no overview of related quantitative findings that were obtained in a large number of real meta-analyses. This study aimed at providing such empirical summary.

From the literature 50 meta-analyses were randomly selected that had reported their 2 × 2 count data of sensitivity and specificity. Descriptive statistics, assessment of between-study heterogeneity and bivariate random-effects meta-analysis of sensitivity and specificity were performed with a novel Bayesian program code. The bivariate model parameters were also converted to the parameters of the closely related hierarchical summary receiver operating characteristic (HSROC) model.

Among the 50 meta-analyses, the studies per meta-analysis ranged from 5 to 45 and the disease prevalence from 2.3 to 71%. Significant between-study heterogeneity was found in 43 of 50 meta-analyses, favouring a random-effects model over a fixed-effects model. Empirical distributions of sensitivity and specificity, positive and negative likelihood ratios, and other model results are presented in the full text numerically and graphically.

Studies of diagnostic test accuracy can be well meta-analysed within a Bayesian framework, and the presented quantitative findings provide an orientation when interpreting the results of the standard bivariate/HSROC model.