Geostatistical hierarchical model for temporally integrated radon measurements

Abstract

In the search for important determinants of disease, epidemiologists often face the challenging task of retrospectively estimating exposures of interest. Such is the case in modern studies of the lung cancer risk posed by residential radon - a naturally occurring radioactive gas. Assessment of past radon exposures is limited because measurements are not generally available for the locations at which study subjects spent time prior to enrollment. In such settings, there is a need for prediction at unmeasured geographic sites and time periods. We develop a hierarchical Bayesian geostatistical model for predicting unmeasured radon concentrations over space and time. Our work arises from a study of residential radon in Iowa, where measurements were taken as yearly averages and subject to detector measurement error. Much attention has been given lately to geostatistical methods for data that are obtained as integrated averages over geographic regions. We show how these techniques work in the time domain as well.Unlike the numerical approximations that are needed to integrate over geographic regions, we are able to provide closed-form solutions for the integration that must be performed over temporal periods. Our approach is illustrated with radon concentrations measured from 614 different geographic sites and 799 time periods. © 2008 American Statistical Association and the International Biometric Society.