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Extensions of Bayesian hierarchical models with applications in medical research

Boek - Dissertatie

Medical research applications often involve hierarchical data structures such as patients grouped in hospitals or teeth clustered within mouths. In the modeling process, it is important to incorporate the correlation among observations belonging to the same cluster. Failing to do so, may lead to incorrect estimation of the variability in the data and misleading inference. The Bayesian approach offers tools to directly account for the hierarchical structure of the data via augmented latent variables. In addition, Bayesian methods allow us to fit flexible models for complex data structures, which may be difficult to estimate based on maximum likelihood. Moreover, the inclusion of tailored priors can solve identifiability issues in elaborate models. The increasing complexity in modern data sets motivates the development of novel models that can accurately describe the data. In this sense, we propose some extensions of Bayesian methods to model hierarchical data. We cover a broad spectrum of topics such as covariance matrix modeling for multivariate outcomes, model selection using the deviance information criterion, multi-group regression methods and factor analysis. The four topics are introduced throughout the chapters of this dissertation. These approaches are motivated in the context of medical sciences but they can be implemented in many other fields in general. We carry out exhaustive simulation studies to demonstrate the advantages of the proposed models compared to the existing methods in the literature. In addition, the procedures are illustrated using the RN4CAST project \citep{serme11} and other well-known data sets. We implement all the models in JAGS running interactively from R. The relevant code can be found online as supplementary material of the published papers.
Jaar van publicatie:2019
Toegankelijkheid:Open