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COVARIATE ADJUSTED MIXTURE MODELS AND DISEASE MAPPING WITH THE PROGRAM DISMAPWIN
Author(s) -
SCHLATTMANN PETER,
DIETZ EKKEHART,
BÖHNING DANKMAR
Publication year - 1996
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/(sici)1097-0258(19960415)15:7/9<919::aid-sim260>3.0.co;2-w
Subject(s) - covariate , bayes' theorem , poisson regression , cluster analysis , statistics , econometrics , poisson distribution , computer science , representation (politics) , regression analysis , bayesian probability , mathematics , medicine , population , environmental health , politics , political science , law
The analysis and recognition of disease clustering in space and its representation on a map is an important problem in epidemiology. An approach using mixture models to identify spatial heterogeneity in disease risk and map construction within an empirical Bayes framework is described. Once heterogeneity is detected, the question arises as how explanatory variables could be included in the model. A mixed Poisson regression approach to include covariates is presented. The methods are illustrated using data for tuberculosis from Berlin in 1991.