Iowa radon leukaemia study: a hierarchical population risk model for spatially correlated exposure measured with error.
Smith-BJ; Zhang-L; Field-RW
Stat Med 2007 Nov; 26(25):4619-4642
This paper presents a Bayesian model that allows for the joint prediction of county-average radon levels and estimation of the associated leukaemia risk. The methods are motivated by radon data from an epidemiologic study of residential radon in Iowa that include 2726 outdoor and indoor measurements. Prediction of county-average radon is based on a geostatistical model for the radon data which assumes an underlying continuous spatial process. In the radon model, we account for uncertainties due to incomplete spatial coverage, spatial variability, characteristic differences between homes, and detector measurement error. The predicted radon averages are, in turn, included as a covariate in Poisson models for incident cases of acute lymphocytic (ALL), acute myelogenous (AML), chronic lymphocytic (CLL), and chronic myelogenous (CML) leukaemias reported to the Iowa cancer registry from 1973 to 2002. Since radon and leukaemia risk are modelled simultaneously in our approach, the resulting risk estimates accurately reflect uncertainties in the predicted radon exposure covariate. Posterior mean (95 per cent Bayesian credible interval) estimates of the relative risk associated with a 1 pCi/L increase in radon for ALL, AML, CLL, and CML are 0.91 (0.78-1.03), 1.01 (0.92-1.12), 1.06 (0.96-1.16), and 1.12 (0.98-1.27), respectively.
Models; Carcinogens; Cancer; Epidemiology; Humans; Men; Women; Statistical-analysis; Risk-factors;
Author Keywords: Bayesian methods; leukaemia risk; Markov chain Monte Carlo; Poisson regression; residential radon exposure; spatial statistics
Brian J. Smith, Department of Biostatistics, The University of Iowa, 200 Hawkins Drive, C22 GH, Iowa City, IA 52242-1009
Statistics in Medicine
University of Iowa