Disease mapping of zero-excessive mesothelioma data in Flanders.

[1]  D. Clayton,et al.  Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. , 1987, Biometrics.

[2]  M. Karim.  Generalized Linear Models With Random Effects , 1991 .

[3]  Norman E. Breslow,et al.  Estimation of Disease Rates in Small Areas: A new Mixed Model for Spatial Dependence , 2000 .

[4]  Ida Scheel,et al.  A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims , 2013, Journal of the Royal Statistical Society. Series C, Applied statistics.

[5]  J. Besag,et al.  On conditional and intrinsic autoregressions , 1995 .

[6]  R. Ocaña-Riola.  Common errors in disease mapping. , 2010, Geospatial health.

[7]  Guangqing Chi,et al.  Applied Spatial Data Analysis with R , 2015 .

[8]  J. Fleiss,et al.  The Standardization of Rates , 2004 .

[9]  B Terracini,et al.  Multicentric study on malignant pleural mesothelioma and non-occupational exposure to asbestos , 2000, British Journal of Cancer.

[10]  M. Bussacos,et al.  The next mesothelioma wave: mortality trends and forecast to 2030 in Brazil. , 2015, Cancer epidemiology.

[11]  I. Opitz.  Management of malignant pleural mesothelioma-The European experience. , 2014, Journal of thoracic disease.

[12]  B Terracini,et al.  Pleural malignant mesothelioma and non-occupational exposure to asbestos in Casale Monferrato, Italy. , 1995, Occupational and environmental medicine.

[13]  A. Burdorf,et al.  Update of predictions of mortality from pleural mesothelioma in the Netherlands , 2003, Occupational and environmental medicine.

[14]  P. Diggle.  Applied Spatial Statistics for Public Health Data , 2005 .

[15]  Magne Aldrin.  Generalized linear mixed models in R , 2013 .

[16]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[17]  A. Keen,et al.  A simple approach for the analysis of generalizea linear mixed models , 1994 .

[18]  T. Nawrot,et al.  Belgium: historical champion in asbestos consumption , 2007, The Lancet.

[19]  L. Beckett,et al.  Residential proximity to naturally occurring asbestos and mesothelioma risk in California. , 2005, American journal of respiratory and critical care medicine.

[20]  G. Molenberghs,et al.  Models for Discrete Longitudinal Data , 2005 .

[21]  N. Breslow.  Extra‐Poisson Variation in Log‐Linear Models , 1984 .

[22]  Alan Agresti,et al.  Random effect models for repeated measures of zero-inflated count data , 2005 .

[23]  B. Nemery,et al.  High risk of malignant mesothelioma and pleural plaques in subjects born close to ophiolites. , 2013, Chest.

[24]  N. Breslow,et al.  Approximate inference in generalized linear mixed models , 1993 .

[25]  M. Camus,et al.  Incidence of malignant mesothelioma of the pleura in Québec and Canada from 1984 to 2007, and projections from 2008 to 2032. , 2015, American journal of industrial medicine.

[26]  G. C. Ferguson,et al.  Mesothelioma due to domestic exposure to asbestos , 1984 .

[27]  J. Mullahy.  Specification and testing of some modified count data models , 1986 .

[28]  Christel Faes,et al.  The bivariate combined model for spatial data analysis , 2016, Statistics in medicine.

[29]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .

[30]  M. Pollán,et al.  Municipal pleural cancer mortality in Spain , 2005, Occupational and Environmental Medicine.

[31]  Neal Alexander,et al.  Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology , 2011 .

[32]  Hong Chang,et al.  Bayesian Approach for Nonlinear Random Effects Models , 1997 .

[33]  P. Deboosere,et al.  Asbestos in Belgium: an underestimated health risk. The evolution of mesothelioma mortality rates (1969–2009) , 2014, International journal of occupational and environmental health.

[34]  Geert Molenberghs,et al.  A generalized Poisson-gamma model for spatially overdispersed data. , 2012, Spatial and spatio-temporal epidemiology.

[35]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[36]  Geert Molenberghs,et al.  Arbitrariness of models for augmented and coarse data, with emphasis on incomplete data and random effects models , 2010 .

[37]  Alan E. Gelfand,et al.  Zero-inflated models with application to spatial count data , 2002, Environmental and Ecological Statistics.

[38]  Diane Lambert,et al.  Zero-inflacted Poisson regression, with an application to defects in manufacturing , 1992 .

[39]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[40]  C. Czado,et al.  Modelling count data with overdispersion and spatial effects , 2008 .

[41]  Paul Janssen,et al.  Frailty Model , 2007, International Encyclopedia of Statistical Science.

[42]  W. Greene,et al.  Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models , 1994 .

[43]  J. Lawless.  Negative binomial and mixed Poisson regression , 1987 .

[44]  Geert Molenberghs,et al.  A Family of Generalized Linear Models for Repeated Measures With Normal and Conjugate Random Effects (pp.188) , 2010, 1101.0990.

[45]  T. Goicoa,et al.  Smoothing and high risk areas detection in space-time disease mapping: a comparison of P-splines, autoregressive, and moving average models , 2016, Stochastic Environmental Research and Risk Assessment.

[46]  A. Agudo,et al.  Occupation and risk of malignant pleural mesothelioma: A case-control study in Spain. , 2000, American journal of industrial medicine.

[47]  R. Wolfinger,et al.  Generalized linear mixed models a pseudo-likelihood approach , 1993 .

[48]  Ana F. Militino,et al.  Testing for Poisson Zero Inflation in Disease Mapping , 2004 .