An intuitive Bayesian spatial model for disease mapping that accounts for scaling

By Andrea Riebler, Sigrunn Holbek Sørbye, Daniel P. Simpson, Håvard Rue. Statistical Methods in Medical Research, 2016

496 citations recorded by the source corpus.

In recent years, disease mapping studies have become a routine application within geographical epidemiology and are typically analysed within a Bayesian hierarchical model formulation. A variety of model formulations for the latent level have been proposed but all come with inherent issues. In the classical BYM (Besag, York and Mollié) model, the spatially structured component cannot be seen independently from the unstructured component. This makes prior definitions for the hyperparameters of the two random effects challenging. There are alternative model formulations that address this confounding; however, the issue on how to choose interpretable hyperpriors is still unsolved. Here, we discuss a recently proposed parameterisation of the BYM model that leads to improved parameter control as the hyperparameters can be seen independently from each other. Furthermore, the need for a scaled spatial component is addressed, which facilitates assignment of interpretable hyperpriors and make these transferable between spatial applications with different graph structures. The hyperparameters themselves are used to define flexible extensions of simple base models. Consequently, penalised complexity priors for these parameters can be derived based on the information-theoretic distance from the flexible model to the base model, giving priors with clear interpretation. We provide implementation details for the new model formulation which preserve sparsity properties, and we investigate systematically the model performance and compare it to existing parameterisations. Through a simulation study, we show that the new model performs well, both showing good learning abilities and good shrinkage behaviour. In terms of model choice criteria, the proposed model performs at least equally well as existing parameterisations, but only the new formulation offers parameters that are interpretable and hyperpriors that have a clear meaning.

Subfields: Economics and Econometrics, Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/072ba78040847cd2f1c86aaa274b23bf795c875d

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Citations in the corpus

Bayesian Computing with INLA: A Review (2016, methodology): “Some examples on application of these priors include disease mapping (Riebler et al., 2016), bivariate meta-analysis (Guo et al., 2015; Guo and Riebler, 2015), age-period-cohort models (Riebler and Held, 2016), Bayesian P-splines (Ventrucci and Rue, 2016), structured additive distributional…”
Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors (2014, background): “The directed acyclic graph–approach pioneered by the WinBUGS inference engine, is now a standard tool for specification of general Bayesian models.”
Spatial modelling with R-INLA: A review (2018, methodology): “This desired behaviour has been shown by Riebler et al. (2016) and is not achievable by other commonly used priors.”