Spatial modeling using frequentist approach for disease mapping

Abstract

In this article, a generalized linear mixed model (GLMM) based on a frequentist approach is employed to examine spatial trend of asthma data. However, the frequentist analysis of GLMM is computationally difficult. On the other hand, the Bayesian analysis of GLMM has been computationally convenient due to the advent of Markov chain Monte Carlo algorithms. Recently developed data cloning (DC) method, which yields to maximum likelihood estimate, provides frequentist approach to complex mixed models and equally computationally convenient method. We use DC to conduct frequentist analysis of spatial models. The advantages of the DC approach are that the answers are independent of the choice of the priors, non-estimable parameters are flagged automatically, and the possibility of improper posterior distributions is completely avoided. We illustrate this approach using a real dataset of asthma visits to hospital in the province of Manitoba, Canada, during 2000–2010. The performance of the DC approach in our application is also studied through a simulation study.

Keywords:

• Bayesian computation
• conditional autoregressive
• disease mapping
• generalized linear mixed model
• geographic epidemiology
• prediction

Acknowledgements

This work was supported by a grant from the Natural Sciences and Engineering Research Council of Canada. The comments of two referees are gratefully acknowledged.\newline Disclaimer: The interpretations, conclusions and opinions expressed in this article are those of the author and do not necessarily reflect the position of Manitoba Health. This study is based in part on data provided by Manitoba Health through Manitoba Centre for Health Policy. The interpretation and conclusions contained herein are those of the researcher and do not necessarily represent the views of the government of Manitoba.