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Jane E Joseph - One of the best experts on this subject based on the ideXlab platform.
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joint space time bayesian Disease Mapping via quantification of Disease risk association
Statistical Methods in Medical Research, 2021Co-Authors: Daniel R Baer, Andrew B Lawson, Jane E JosephAbstract:Alzheimer’s Disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer’s Disease risk in order to pre...
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joint space time bayesian Disease Mapping via quantification of Disease risk association
Statistical Methods in Medical Research, 2021Co-Authors: Daniel R Baer, Andrew B Lawson, Jane E JosephAbstract:Alzheimer's Disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer's Disease risk in order to preclude its inception in patients. Characterizing Alzheimer's Disease risk can be accomplished at the population-level by the space-time modeling of Alzheimer's Disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer's Disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer's Disease risk over space and time. From an application of these models to real-world Alzheimer's Disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer's Disease risk.
Andrew B Lawson - One of the best experts on this subject based on the ideXlab platform.
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joint space time bayesian Disease Mapping via quantification of Disease risk association
Statistical Methods in Medical Research, 2021Co-Authors: Daniel R Baer, Andrew B Lawson, Jane E JosephAbstract:Alzheimer’s Disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer’s Disease risk in order to pre...
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joint space time bayesian Disease Mapping via quantification of Disease risk association
Statistical Methods in Medical Research, 2021Co-Authors: Daniel R Baer, Andrew B Lawson, Jane E JosephAbstract:Alzheimer's Disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer's Disease risk in order to preclude its inception in patients. Characterizing Alzheimer's Disease risk can be accomplished at the population-level by the space-time modeling of Alzheimer's Disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer's Disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer's Disease risk over space and time. From an application of these models to real-world Alzheimer's Disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer's Disease risk.
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gaussian component mixtures and car models in bayesian Disease Mapping
ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS); Science & Engineering Faculty, 2012Co-Authors: Paula Moraga, Andrew B LawsonAbstract:Hierarchical Bayesian models involving conditional autoregression (CAR) components are commonly used in Disease Mapping. An alternative model to the proper or improper CAR is the Gaussian component mixture (GCM) model. A review of CAR and GCM models is provided in univariate settings where only one Disease is considered, and also in multivariate situations where in addition to the spatial dependence between regions, the dependence among multiple Diseases is analyzed. A performance comparison between models using a set of simulated data to help illustrate their respective properties is reported. The results show that both in univariate and multivariate settings, both models perform in a comparable way under a wide range of conditions. GCM and CAR models are applied for estimating the relative risk of low birth weight in Georgia, USA, in the year 2000.
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bayesian Disease Mapping hierarchical modeling in spatial epidemiology
2008Co-Authors: Andrew B LawsonAbstract:BACKGROUND Introduction Data Sets Bayesian Inference and Modeling Likelihood Models Prior Distributions Posterior Distributions Predictive Distributions Bayesian Hierarchical Modeling Hierarchical Models Posterior Inference Exercises Computational Issues Posterior Sampling Markov Chain Monte Carlo Methods Metropolis and Metropolis-Hastings Algorithms Gibbs Sampling Perfect Sampling Posterior and Likelihood Approximations Exercises Residuals and Goodness-of-Fit Model Goodness-of-Fit Measures General Residuals Bayesian Residuals Predictive Residuals and the Bootstrap Interpretation of Residuals in a Bayesian Setting Exceedence Probabilities Exercises THEMES Disease Map Reconstruction and Relative Risk Estimation An Introduction to Case Event and Count Likelihoods Specification of the Predictor in Case Event and Count Models Simple Case and Count Data Models with Uncorrelated Random Effects Correlated Heterogeneity Models Convolution Models Model Comparison and Goodness-of-Fit Diagnostics Alternative Risk Models Edge Effects Exercises Disease Cluster Detection Cluster Definitions Cluster Detection using Residuals Cluster Detection using Posterior Measures Cluster Models Edge Detection and Wombling Ecological Analysis General Case of Regression Biases and Misclassification Error Putative Hazard Models Multiple Scale Analysis Modifiable Areal Unit Problem (MAUP) Misaligned Data Problem (MIDP) Multivariate Disease Analysis Notation for Multivariate Analysis Two Diseases Multiple Diseases Spatial Survival and Longitudinal Analyses General Issues Spatial Survival Analysis Spatial Longitudinal Analysis Extensions to Repeated Events Spatiotemporal Disease Mapping Case Event Data Count Data Alternative Models Infectious Diseases Appendix A: Basic R and WinBUGS Appendix B: Selected WinBUGS Code Appendix C: R Code for Thematic Mapping References Index
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Disease Mapping with winbugs and mlwin
2003Co-Authors: Andrew B Lawson, William J Browne, Carmen Vidal L RodeiroAbstract:Preface. Notation. 0.1 Standard notation for multilevel modelling. 0.2 Spatial multiple-membership models and the MMMC notation. 0.3 Standard notation for WinBUGS models. 1. Disease Mapping basics. 1.1 Disease Mapping and map reconstruction. 1.2 Disease map restoration. 2. Bayesian hierarchical modelling. 2.1 Likelihood and posterior distributions. 2.2 Hierarchical models. 2.3 Posterior inference. 2.4 Markov chain Monte Carlo methods. 2.5 Metropolis and Metropolis-Hastings algorithms. 2.6 Residuals and goodness of fit. 3. Multilevel modelling. 3.1 Continuous response models. 3.2 Estimation procedures for multilevel models. 3.3 Poisson response models. 3.4 Incorporating spatial information. 3.5 Discussion. 4. WinBUGS basics. 4.1 About WinBUGS. 4.2 Start using WinBUGS. 4.3 Specification of the model. 4.4 Model fitting. 4.5 Scripts. 4.6 Checking convergence. 4.7 Spatial modelling: GeoBUGS. 4 .8 Conclusions. 5. MLwiN basics. 5.1 About MLwiN. 5.2 Getting started. 5.3 Fitting statistical models. 5.4 MCMC estimation in MLwiN. 5.5 Spatial modelling. 5.6 Conclusions. 6. Relative risk estimation. 6.1 Relative risk estimation using WinBUGS. 6.2 Spatial prediction. 6.3 An analysis of the Ohio dataset using MLwiN. 7. Focused clustering: the analysis of putative health hazards. 7.1 Introduction. 7.2 Study design. 7.3 Problems of inference. 7.4 Modelling the hazard exposure risk. 7.5 Models for count data. 7.6 Bayesian models. 7.7 Focused clustering in WinBUGS. 7.8 Focused clustering in MLwiN. 8. Ecological analysis. 8.1 Introduction. 8.2 Statistical models. 8.3 WinBUGS analyses of ecological datasets. 8.4 MLwiN analyses of ecological datasets. 9. Spatially-correlated survival analysis. 9.1 Survival analysis in WinBUGS. 9.2 Survival analysis in MLwiN. 10. Epilogue. Appendix 1: WinBUGS code for focused clustering models. A.1: Falkirk example. A.2: Ohio example. Appendix 2: S-Plus function for conversion to GeoBUGS format. Bibliography. Index.
Nicky Best - One of the best experts on this subject based on the ideXlab platform.
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bayesian latent variable modelling of multivariate spatio temporal variation in cancer mortality
Statistical Methods in Medical Research, 2008Co-Authors: Evangelia Tzala, Nicky BestAbstract:In this article, three alternative Bayesian hierarchical latent factor models are described for spatially and temporally correlated multivariate health data. The fundamentals of factor analysis with ideas of space- time Disease Mapping to provide a flexible framework for the joint analysis of multiple-related Diseases in space and time with a view to estimating common and Disease-specific trends in cancer risk are combined. The models are applied to area-level mortality data on six diet-related cancers for Greece over the 20-year period from 1980 to 1999. The aim of this study is to uncover the spatial and temporal patterns of any latent factor(s) underlying the cancer data that could be interpreted as reflecting some aspects of the habitual diet of the Greek population.
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a comparison of bayesian spatial models for Disease Mapping
Statistical Methods in Medical Research, 2005Co-Authors: Nicky Best, Sylvia Richardson, Andrew ThomsonAbstract:With the advent of routine health data indexed at a fine geographical resolution, small area Disease Mapping studies have become an established technique in geographical epidemiology. The specific issues posed by the sparseness of the data and possibility for local spatial dependence belong to a generic class of statistical problems involving an underlying (latent) spatial process of interest corrupted by observational noise. These are naturally formulated within the framework of hierarchical models, and over the past decade, a variety of spatial models have been proposed for the latent level(s) of the hierarchy. In this article, we provide a comprehensive review of the main classes of such models that have been used for Disease Mapping within a Bayesian estimation paradigm, and report a performance comparison between representative models in these classes, using a set of simulated data to help illustrate their respective properties. We also consider recent extensions to model the joint spatial distribution of multiple Disease or health indicators. The aim is to help the reader choose an appropriate structural prior for the second level of the hierarchical model and to discuss issues of sensitivity to this choice.
Daniel R Baer - One of the best experts on this subject based on the ideXlab platform.
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joint space time bayesian Disease Mapping via quantification of Disease risk association
Statistical Methods in Medical Research, 2021Co-Authors: Daniel R Baer, Andrew B Lawson, Jane E JosephAbstract:Alzheimer’s Disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer’s Disease risk in order to pre...
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joint space time bayesian Disease Mapping via quantification of Disease risk association
Statistical Methods in Medical Research, 2021Co-Authors: Daniel R Baer, Andrew B Lawson, Jane E JosephAbstract:Alzheimer's Disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer's Disease risk in order to preclude its inception in patients. Characterizing Alzheimer's Disease risk can be accomplished at the population-level by the space-time modeling of Alzheimer's Disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer's Disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer's Disease risk over space and time. From an application of these models to real-world Alzheimer's Disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer's Disease risk.
Archie C A Clements - One of the best experts on this subject based on the ideXlab platform.
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application of knowledge driven spatial modelling approaches and uncertainty management to a study of rift valley fever in africa
International Journal of Health Geographics, 2006Co-Authors: Archie C A Clements, D U Pfeiffer, Vincent MartinAbstract:Background There are few studies that have investigated uncertainties surrounding the scientific community's knowledge of the geographical distribution of major animal Diseases. This is particularly relevant to Rift Valley fever (RVF), a zoonotic Disease causing destructive outbreaks in livestock and man, as the geographical range of the Disease is widening to involve previously unaffected regions. In the current study we investigate the application of methods developed in the decision sciences: multiple criteria decision making using weighted linear combination and ordered weighted averages, and Dempster-Shafer theory, implemented within the geographical information system IDRISI, to obtain a greater understanding of uncertainty related to the geographical distribution of RVF. The focus is on presenting alternate methods where extensive field data are not available and traditional, model-based approaches to Disease Mapping are impossible to conduct.
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bayesian spatial analysis and Disease Mapping tools to enhance planning and implementation of a schistosomiasis control programme in tanzania
Tropical Medicine & International Health, 2006Co-Authors: Alan Fenwick, Archie C A Clements, Nicholas J S Lwambo, Lynsey Blair, Ursuline Nyandindi, Godfrey M Kaatano, Safari Kinunghi, Joanne P Webster, Simon BrookerAbstract:OBJECTIVE: To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania. METHODS: Bayesian geostatistical models were developed using parasitological data from 143 schools. RESULTS: In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation. CONCLUSION: Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.
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bayesian spatial analysis and Disease Mapping tools to enhance planning and implementation of a schistosomiasis control programme in tanzania
Tropical Medicine & International Health, 2006Co-Authors: Alan Fenwick, Archie C A Clements, Nicholas J S Lwambo, Lynsey Blair, Ursuline Nyandindi, Godfrey M Kaatano, Safari Kinunghi, Joanne P Webster, Simon BrookerAbstract:OBJECTIVE: To predict the spatial distributions of Schistosoma haematobium and S. mansoni infections to assist planning the implementation of mass distribution of praziquantel as part of an on-going national control programme in Tanzania. METHODS: Bayesian geostatistical models were developed using parasitological data from 143 schools. RESULTS: In the S. haematobium models, although land surface temperature and rainfall were significant predictors of prevalence, they became non-significant when spatial correlation was taken into account. In the S. mansoni models, distance to water bodies and annual minimum temperature were significant predictors, even when adjusting for spatial correlation. Spatial correlation occurred over greater distances for S. haematobium than for S. mansoni. Uncertainties in predictions were examined to identify areas requiring further data collection before programme implementation. CONCLUSION: Bayesian geostatistical analysis is a powerful and statistically robust tool for identifying high prevalence areas in a heterogeneous and imperfectly known environment.