Bayesian Bivariate Diagnostic Meta-analysis via R-INLA

Ben Dwamena and Harvard Rue
Additional contact information
Harvard Rue: Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway

BOS10 Stata Conference from Stata Users Group

Abstract: Bivariate generalized mixed modeling is currently recommended for joint meta-analysis of diagnostic test sensitivity and specificity. Estimation is commonly performed using frequentist likelihood-based techniques assuming bivariate normally distributed, correlated logit transformations of sensitivity and specificity. These estimation techniques are fraught with non-convergence and invalid confidence intervals and correlation parameters especially with sparse data. Bayesian approaches, though likely to surmount these and other problems, have not previously been applied. Recently, integrated nested laplacian approximation has been developed (INLA)as a computationally fast, deterministic alternative to Markov chain Monte Carlo (MCMC)-based bayesian modeling and an R interface to the C-based inla program has been applied to diagnostic Meta-analysis. This presentation shows how to facilely interface R-INLA estimation with data pre- and post-processing within Stata. A user-written ado-file allows user-friendly application of INLA by Stata users.

Date: 2010-07-20
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:boc:bost10:13

Access Statistics for this paper

More papers in BOS10 Stata Conference from Stata Users Group Contact information at EDIRC.
Bibliographic data for series maintained by Christopher F Baum ().