On the impact of covariate measurement error on spatial regression modelling.
Spatial regression models have grown in popularity in response to rapid advances in GIS (Geographic Information Systems) technology that allows epidemiologists to incorporate geographically indexed data into their studies. However, it turns out that there are some subtle pitfalls in the use of these models. We show that presence of covariate measurement error can lead to significant sensitivity of parameter estimation to the choice of spatial correlation structure. We quantify the effect of measurement error on parameter estimates, and then suggest two different ways to produce consistent estimates. We evaluate the methods through a simulation study. These methods are then applied to data on Ischemic Heart Disease (IHD).
School of Mathematical Sciences, University of Technology, Sydney.
Md Hamidul
MH
Huque
Department of Statistics, North Carolina State University,Raleigh, NC 27695, U.S.A.
Howard
H
Bondell
School of Mathematical Sciences, University of Technology, Sydney.
Louise
L
Ryan
Attenuation; Environmetal epidemiology; Geostatistics; Measurement Error; Mixed models; Random effects; SEIFA; Sensitivity; Spatial correlation; Spatial linear regression