Mohammadreza Mohebbi|Rory Wolfe|Damien Jolley
A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations
Monash University|Monash University|Monash University
analytic methods commonly used epidemiology account spatial correlation observations regression analyses omission autocorrelation bias parameter estimates yield incorrect standard error estimateswe used age standardised incidence ratios sirs esophageal cancer ec babol cancer registry 2001 2005 extracted socioeconomic indices statistical centre iran following models sir used 1 poisson regression agglomerationspecific nonspatial random effects 2 poisson regression agglomerationspecific spatial random effects distancebased neighbourhoodbased autocorrelation structures used defining spatial random effects pseudolikelihood approach applied estimate model parameters bayesian information criterion bic akaikes information criterion aic adjusted pseudo r2 used model comparisona gaussian semivariogram effective range 225 km best fit spatial autocorrelation agglomerationlevel ec incidence morans index greater expected value indicating systematic geographical clustering ec distancebased neighbourhoodbased poisson regression estimates generally similar residual spatial dependence modelled point interval estimates covariate effects different obtained nonspatial poisson modelthe spatial pattern evident ec sir observation point estimates standard errors differed depending modelling approach indicate importance accounting residual spatial correlation analyses ec incidence caspian region iran results also illustrate spatial smoothing must applied care