Modelling risk from a disease in time and space

Abstract

This paper combines existing models for longitudinal and spatial data in a hierarchical Bayesian framework, with particular emphasis on the role of time- and space-varying covariate effects. Data analysis is implemented via Markov chain Monte Carlo methods. The methodology is illustrated by a tentative re-analysis of Ohio lung cancer data 1968-1988. Two approaches that adjust for unmeasured spatial covariates, particularly tobacco consumption, are described. The first includes random effects in the model to account for unobserved heterogeneity; the second adds a simple urbanization measure as a surrogate for smoking behaviour. The Ohio data set has been of particular interest because of the suggestion that a nuclear facility in the southwest of the state may have caused increased levels of lung cancer there. However, we contend here that the data are inadequate for a proper investigation of this issue.

Trends of Breast Cancer Incidence in Iran During 2004-2008: A Bayesian Space-time Model

“…To circumvent problems related to SMR and SIR, several methods have been proposed and applied in a variety of disciplines (Besag et al, 1991;Bernardinelli et al, 1995b;Bernardinelli et al, 1997;Knorr-Held, 1998;Maiti, 1998;Bohning et al, 2000;Knorr-Held, 2000;Lawson et al, 2000;MacNab and Dean;2002). Bayesian models have received great attention amongst the others due to their ability in modeling complex spatial data.…”

“…Bernardinelli et al (1995b), Waller et al (1997), Knorr-Held et al (1998), and(KnorrHeld andBesag, 1998) used Bayesian methodology to model the disease risk in space and over time.…”

Bayesian statistics in medicine: a 25 year review

“…A review of issues in the analysis of small area health data shows the impact Bayesian methods have made in providing a systematic framework [324]. Work included identifying geographical areas with excess incidence of leukaemia [325], mapping of lung cancer mortality in Ohio with errors in covariates [326], and combining longitudinal and spatial data [327] built on previous work on mapping spatial smoothing of survival from breast cancer and malignant melanoma [328]. Other applications included spatiotemporal analysis of lung cancer rates in Missouri [329].…”

Spatial statistical methods in health

“…Several authors have also considered how models can be extended to handle disease incidence data which has a temporal, as well as a spatial dimension (e.g. Bernardinelli et al, 1995b;Knorr-Held & Besag, 1998;Waller et al, 1997). Special problems introduced by edge effects in disease mapping have been discussed by Lawson et al (1999b).…”

“…Ecological models appropriate for spatio-temporal data have also been considered (e.g. Bernardinelli et al, 1995b;Knorr-Held & Besag, 1998;Waller et al, 1997;Wikle et al, 1998). Methods for longitudinal data in general (e.g.…”