Dataset for: Comparing hierarchical models via the marginalized deviance information criterion

Hierarchical models are extensively used in pharmacokinetics and longitudinal studies. When the estimation is carried out from a Bayesian approach, model comparison is often based on the deviance information criterion (DIC). In hierarchical models with latent variables there are several versions of this statistic, specifically: the conditional DIC (cDIC) that incorporates the latent variables in the focus of the analysis and the marginalized DIC (mDIC) which integrates them out. Regardless of the asymptotic and coherency difficulties of cDIC, this alternative is usually employed in Markov chain Monte Carlo (MCMC) methods for hierarchical models due to practical convenience. The mDIC criterion is more appropriate in most cases but requires integration of the likelihood which is computationally demanding and not implemented in Bayesian software. Therefore, we consider a method to compute mDIC by generating replicate samples of the latent variables that need to be integrated out. This alternative can be easily conducted from the MCMC output of Bayesian packages and is widely applicable to hierarchical models in general. Additionally, we propose some approximations in order to reduce the computational complexity for large-sample situations. The method is illustrated with simulated data sets and two medical studies, evidencing that cDIC may be misleading whilst mDIC appears pertinent.