Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations

M. Paul*, A. Riebler, L. M. Bachmann, H. Rue, L. Held

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1325-1339
Number of pages15
JournalStatistics in medicine
Issue number12
StatePublished - May 30 2010
Externally publishedYes


  • Bayesian methods
  • Bivariate random effects model
  • Diagnostic studies
  • Integrated nested Laplace approximation (INLA)
  • Meta-analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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