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Bayesian Bivariate Diagnostic Meta-analysis via R-INLA


  • Ben Adarkwa Dwamena

    (University of Michigan and VA Ann Arbor Health Systems)

  • Harvard Rue

    (Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim, Norway)


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.

Suggested Citation

  • Ben Adarkwa Dwamena & Harvard Rue, 2010. "Bayesian Bivariate Diagnostic Meta-analysis via R-INLA," BOS10 Stata Conference 13, Stata Users Group.
  • Handle: RePEc:boc:bost10:13

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    Cited by:

    1. Ben Adarkwa Dwamena, 2015. "midasinla: midas goes bayesian via R-INLA," 2015 Stata Conference 22, Stata Users Group.

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