Paul M Riebler A Bachmann LM Rue H Held L 2010 For bivariate meta-analysis of diagnostic studies, likelihood approaches are very popular. However, they often run into numerical problems with possible non-convergence. In addition, the construction of confidence intervals is controversial. Bayesian methods based on Markov chain Monte Carlo (MCMC) sampling could be used, but are often difficult to implement, and require long running times and diagnostic convergence checks. Recently, a new Bayesian deterministic inference approach for latent Gaussian models using integrated nested Laplace approximations (INLA) has been proposed. With this approach MCMC sampling becomes redundant as the posterior marginal distributions are directly and accurately approximated. By means of a real data set we investigate the influence of the prior information provided and compare the results obtained by INLA, MCMC, and the maximum likelihood procedure SAS PROC NLMIXED. Using a simulation study we further extend the comparison of INLA and SAS PROC NLMIXED by assessing their performance in terms of bias, mean-squared error, coverage probability, and convergence rate. The results indicate that INLA is more stable and gives generally better coverage probabilities for the pooled estimates and less biased estimates of variance parameters. The user-friendliness of INLA is demonstrated by documented R-code. https://sonar.ch/global/documents/85401 eng info:eu-repo/semantics/altIdentifier/doi/10.1002/sim.3858 info:eu-repo/semantics/altIdentifier/pmid/20101670 Statistics in medicine. - 2010 Bayes Theorem Bias Biomarkers, Tumor Biostatistics Confidence Intervals Diagnostic Tests, Routine Humans Likelihood Functions Linear Models Markov Chains Meta-Analysis as Topic Models, Statistical Monte Carlo Method Sensitivity and Specificity Telomerase Urinary Bladder Neoplasms Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations. http://purl.org/coar/resource_type/c_6501