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In a bivariate meta-analysis the number of diagnostic studies involved isoften very low so that frequentist methods may result in problems. Bayesianinference is attractive as informative priors that add small amount ofinformation can stabilise the analysis without overwhelming the data. However,Bayesian analysis is often computationally demanding and the selection of theprior for the covariance matrix of the bivariate structure is crucial withlittle data. The integrated nested Laplace approximations (INLA) methodprovides an efficient solution to the computational issues by avoiding anysampling, but the important question of priors remain. We explore the penalisedcomplexity (PC) prior framework for specifying informative priors for thevariance parameters and the correlation parameter. PC priors facilitate modelinterpretation and hyperparameter specification as expert knowledge can beincorporated intuitively. We conduct a simulation study to compare theproperties and behaviour of differently defined PC priors to currently usedpriors in the field. The simulation study shows that the use of PC priorsresults in more precise estimates when specified in a sensible neighbourhoodaround the truth. To investigate the usage of PC priors in practice wereanalyse a meta-analysis using the telomerase marker for the diagnosis ofbladder cancer.

Bayesian bivariate meta-analysis of diagnostic test studies with interpretable priors