Evaluation of Bayesian spatio-temporal latent models in small area health data.
ABSTRACT Health outcomes are linked to air pollution, demographic, or socioeconomic factors which vary across space and time. Thus, it is often found that relative risks in space-time health data have locally different temporal patterns. In such cases, latent modeling is useful in the disaggregation of risk profiles. In particular, spatio-temporal mixture models can help to isolate spatial clusters each of which has a homogeneous temporal pattern in relative risks. In mixture modeling, various weight structures can be used and two situations can be considered: the number of underlying components is known or unknown. In this paper, we compare spatio-temporal mixture models with different weight structures in both situations. In addition, spatio-temporal Dirichlet process mixture models are compared to them when the number of components is unknown. For comparison, we propose a set of spatial cluster detection diagnostics based on the posterior distribution of the weights. We also develop new accuracy measures to assess the recovery of true relative risks. Based on the simulation study, we examine the performance of various spatio-temporal mixture models in terms of proposed methods and goodness-of-fit measures. We apply our models to a county-level chronic obstructive pulmonary disease data set from the state of Georgia.
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ABSTRACT: Identifying homogeneous groups of individuals is an important problem in population genetics. Recently, several methods have been proposed that exploit spatial information to improve clustering algorithms. In this article, we develop a Bayesian clustering algorithm based on the Dirichlet process prior that uses both genetic and spatial information to classify individuals into homogeneous clusters for further study. We study the performance of our method using a simulation study and use our model to cluster wolverines in Western Montana using microsatellite data.Biometrics 06/2011; 67(2):381-90. · 1.83 Impact Factor
Article: Space-time models with time-dependent covariates for the analysis of the temporal lag between socioeconomic factors and lung cancer mortality.[show abstract] [hide abstract]
ABSTRACT: The relationship between socioeconomic factors and mortality for lung cancer is investigated. To identify the proper lag time between socioeconomic factors and lung cancer mortality, a space-time hierarchical Bayesian model with time-dependent covariates is adopted. A real example on lung cancer mortality, males, in Tuscany (Italy) during the period 1971-1999, is provided. Results confirm the presence of an association between mortality for lung cancer and socioeconomic factors with a temporal lag (latency time) of at least 10 years.Statistics in Medicine 07/2005; 24(12):1919-32. · 1.88 Impact Factor
Article: Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK).[show abstract] [hide abstract]
ABSTRACT: Recent advances in disease mapping have focused first on including the time dimension, thus giving rise to spatio-temporal analysis of the variation of disease risk and, secondly, on carrying out joint analysis of two diseases that share common environmental risk factors and are, therefore, related. Here, we try to combine both issues and present a joint analysis of the spatio-temporal variation of the risks of two related diseases processes-male and female lung cancer incidence-in a region of England. To do so, we use a Bayesian hierarchical model that splits the risk of disease into two spatio-temporal components: a shared component and a specific component that calibrates the differential between the two diseases.Statistical Methods in Medical Research 09/2006; 15(4):385-407. · 2.44 Impact Factor