Cluster modelling of disease incidence via RJMCMC methods: a comparative evaluation. Reversible jump Markov chain Monte Carlo.
The spatial modelling of small area health data has, for some time, included spatial autocorrelation as a random effect. This effect is non-specific and global and does not address the location of clusters of disease (a specific task). This paper addresses the need for specific and non-specific random effects within spatial epidemiology. In addition, individual frailty is also considered important and a computational algorithm based on reversible jump Markov chain Monte Carlo (RJMCMC) methods is described.
Lawson
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Department of Mathematical Sciences, University of Aberdeen, Aberdeen, AB24 3UE, UK.
D000465:Algorithms; D002648:Child; D004739:England / Q000453:epidemiology; D006801:Humans; D007822:Laryngeal Neoplasms / Q000453:epidemiology*; D008377:Maps as Topic; D008390:Markov Chains; D009010:Monte Carlo Method; D018570:Risk Assessment; D017062:Small-Area Analysis