Space-time mixture modelling of public health data.

This paper aims to enlarge the usual scope of disease mapping by means of dynamic mixtures (DMDM) in case a time component is involved in the data. A special mixture model is suggested which looks for space-time components (clusters) simultaneously. The idea is illustrated using data on female lung cancer from the East German cancer registry for 1960-1989. The conventional mixed Poisson regression model is used as a third model for comparison. The models are discussed in terms of their benefits, difficulties and ease in interpretation, as well as their statistical meaning. Some ideas on evaluation of these models are also included.

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