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CSDA
2004

An evaluation of non-parametric relative risk estimators for disease maps

13 years 3 months ago
An evaluation of non-parametric relative risk estimators for disease maps
In geographical epidemiology it is often required to produce a map of the risk of disease over a study region, a disease map. This paper reviews a variety of approaches to produce disease maps when individual address locations are observed. These methods vary from kernel based smoothing approaches, e.g. Nadaraya-Watson, local linear and GAMs, to Bayesian partition models. The kernel based methods have the advantage of speed, but the partition model has the advantage of being able to adapt to local features (i.e. clustering) of the surface. Another advantage of the kernel based methodology is that the local linear model has a built in edge correction. A simulation study designed to assess the benefits of using an edge corrected estimator for relative risk estimation is described. Keywords and phrases: disease mapping, generalised additive model, partition model, MCMC,
Allan B. Clark, Andrew B. Lawson
Added 17 Dec 2010
Updated 17 Dec 2010
Type Journal
Year 2004
Where CSDA
Authors Allan B. Clark, Andrew B. Lawson
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