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Space‐time mixture modelling of public health data
Author(s) -
Böhning Dankmar,
Dietz Ekkehart,
Schlattmann Peter
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/1097-0258(20000915/30)19:17/18<2333::aid-sim573>3.0.co;2-q
Subject(s) - computer science , scope (computer science) , poisson regression , german , interpretation (philosophy) , poisson distribution , econometrics , meaning (existential) , component (thermodynamics) , regression , statistical model , space (punctuation) , regression analysis , data mining , statistics , mathematics , artificial intelligence , machine learning , medicine , geography , psychology , environmental health , population , physics , archaeology , psychotherapist , thermodynamics , programming language , operating system
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. Copyright © 2000 John Wiley & Sons, Ltd.