Bivariate Random-Effects Meta-analysis of Sensitivity and Specificity with the Bayesian SAS PROC MCMC

Background and Objective: Meta-analysis allows for summarizing the sensitivities and specificities from several primary diagnostic test accuracy studies quantitatively. This article presents and evaluates a full Bayesian method for bivariate random-effects meta-analysis of sensitivity and specificity with SAS PROC MCMC. Methods: First, the formula of the bivariate random-effects model is presented. Then its implementation with the Bayesian SAS PROC MCMC is empirically evaluated, using the published 2 × 2 count data of 50 meta-analyses. The convergence of the Markov chains is analyzed visually and qualitatively. The results are compared with a Bayesian WinBUGS approach, using the Bland-Altman analysis for assessing agreement between 2 methods. Results: The 50 meta-analyses covered broad ranges of pooled sensitivity (17.4% to 98.8%) and specificity (60.0% to 99.7%), and the between-study heterogeneity varied as well. In all meta-analyses, the Markov chains converged well. The meta-analytic results from the SAS PROC MCMC and the WinBUGS random-effects approaches were nearly similar, showing close 95% limits of agreement for the pooled sensitivity (–0.06% to 0.05%) and specificity (–0.05% to 0.05%) without significant differences (P > 0.05). This indicates that the bivariate model is well implemented with both different statistical programs, without systematic differences arising from program attributes. Conclusions: As alternative to a WinBUGS approach, the Bayesian SAS PROC MCMC is well suited for bivariate random-effects meta-analysis of sensitivity and specificity.