Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations
Article first published online: 25 JAN 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 29, Issue 12, pages 1325–1339, 30 May 2010
How to Cite
Paul, M., Riebler, A., Bachmann, L. M., Rue, H. and Held, L. (2010), Bayesian bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximations. Statist. Med., 29: 1325–1339. doi: 10.1002/sim.3858
- Issue published online: 19 MAY 2010
- Article first published online: 25 JAN 2010
- Manuscript Accepted: 21 DEC 2009
- Manuscript Received: 6 FEB 2009
- integrated nested Laplace approximation (INLA);
- bivariate random effects model;
- diagnostic studies;
- Bayesian methods
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. Copyright © 2010 John Wiley & Sons, Ltd.