Hidden Markov models for disease mapping

Peter J. Green (Bristol) & Sylvia Richardson (Imperial College, London)
We present new methodology to extend Hidden Markov models to the spatial domain, and use this class of models to analyse spatial heterogeneity of count data on a rare phenomenon. This situation occurs commonly in many domains of application, particularly in disease mapping.

We assume that the counts follow a Poisson model at the lowest level of the hierarchy, and introduce a finite mixture model for the Poisson rates at the next level. The novelty lies in the model for allocation to the mixture components, which follows a spatially correlated process, the Potts model, and in treating the number of components of the spatial mixture as unknown. Inference is performed in a Bayesian framework using reversible jump MCMC. The model introduced can be viewed as a Bayesian semiparametric approach to specifying flexible spatial distribution in hierarchical models.

Performance of the model and comparison with an alternative well-known Markov random field specification for the Poisson rates are demonstrated on synthetic data sets. We show that our allocation model avoids the problem of oversmoothing in cases where the underlying rates exhibit discontinuities, while giving equally good results in cases of smooth gradient-like or highly autocorrelated rates. The methodology is illustrated on an epidemiological application to data on a rare cancer in France.

Some key words: Allocation, Bayesian hierarchical model, Disease mapping, Finite mixture distributions, Heterogeneity, Hidden Markov models, Markov chain Monte Carlo, Poisson mixtures, Potts model, Reversible jump algorithms, Semiparametric model, Spatial mixtures, Split/merge moves.
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