| Description |
1 online resource : text file, PDF |
| Series |
Chapman & Hall/CRC texts in statistical science series |
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Texts in statistical science.
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| Contents |
Why spatio-temporal epidemiology?; Overview; Health-exposure models; Dependencies over space and time; Examples of spatio-temporal epidemiological analyses; Bayesian hierarchical models; Spatial data; Good spatio-temporal modelling approaches Modelling health risks; Overview; Types of epidemiological study; Measures of risk; Standardised mortality ratios (SMRs); Generalised linear models; Generalised additive models; Generalised estimating equations; Poisson models for count data; Estimating relative risks in relation to exposures; Modelling the cumulative effects of exposure; Logistic models for case-controls studies The importance of uncertainty ; Overview; The wider world of uncertainty; Quantitative uncertainty; Methods for assessing uncertainty; Quantifying uncertainty Embracing uncertainty: the Bayesian approach ; Overview |
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; Introduction to Bayesian inference; Exchangeability; Using the posterior for inference; Predictions; Transformations of parameters; Prior formulation The Bayesian approach in practice ; Overview; Analytical approximations; Markov chain Monte Carlo (MCMC); Using samples for inference; WinBUGS; INLA Strategies for modelling ; Overview; Contrasts; Hierarchical models; Generalised linear mixed models; Linking exposure and health models; Model selection and comparison; What about the p-value?; Comparison of models--Bayes factors; Bayesian model averaging Is 'real' data always quite so real?; Overview; Missing Values; Measurement error; Preferential sampling Spatial patterns in disease ; Overview; The Markov random field (MRF); The conditional autoregressive (CAR) model; Spatial models for disease |
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Mapping From points to fields: modelling environmental hazards over space ; Overview; A brief history of spatial modelling; Exploring spatial data; Modelling spatial data; Spatial trend; Spatial prediction; Stationary and isotropic spatial processes; Variograms; Fitting variogram models; Kriging; Extensions of simple kriging; A hierarchical model for spatially varying exposures; INLA and spatial modelling in a continuous domain; Non-stationary random fields Why time also matters ; Overview; Time series epidemiology; Time series modelling; Modelling the irregular components; The spectral representation theorem and Bochner's lemma; Forecasting; State space models; A hierarchical model for temporally varying exposures The interplay between space and time in exposure assessment ; Overview; Strategies; Spatio-temporal models; Dynamic linear models for space |
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And time; An empirical Bayes approach; A hierarchical model for spatio-temporal exposure data; Approaches to modelling non-separable processes Roadblocks on the way to causality: exposure pathways, |
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Aggregation and other sources of bias; Overview; Causality; Ecological bias; Acknowledging ecological bias; Exposure pathways; Personal exposure models Better exposure measurements through better design ; Overview; Design objectives?; Design paradigms; Geometry-based designs; Probability-based designs; Model-based; An entropy-based approach; Implementation challenges New frontiers; Overview; Non-stationary fields; Physical-statistical modelling; The problem of extreme values Appendix 1: Distribution theory ; Appendix 2: Entropy decomposition References Index Author index A Summary and Exercises appear at the end of each chapter |
| Summary |
"Spatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and environmental epidemiologists, the book links recent developments in spatio-temporal methodology with epidemiological applications. Drawing on real-life problems, it provides the necessary tools to exploit advances in methodology when assessing the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. Designed for graduate students in both epidemiology and statistics, the text covers a wide range of topics, from an introduction to epidemiological principles and the foundations of spatio-temporal modeling to new research directions. It describes traditional and Bayesian approaches and presents the theory of spatial, temporal, and spatio-temporal modeling in the context of its application to environmental epidemiology. The text includes practical examples together with embedded R code, details of specific R packages, and the use of other software, such as WinBUGS/OpenBUGS and integrated nested Laplace approximations (INLA). A supplementary website provides additional code, data, examples, exercises, lab projects, and more."--Publisher's description |
| Notes |
"A Chapman & Hall book." |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Environmental health -- Research -- Methodology
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Epidemiology -- Research -- Methodology
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Spatial analysis (Statistics)
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Environmental Illness -- epidemiology
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Spatio-Temporal Analysis
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Risk Assessment -- methods
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spatial analysis.
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MEDICAL -- Forensic Medicine.
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MEDICAL -- Preventive Medicine.
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MEDICAL -- Public Health.
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Environmental health -- Research -- Methodology
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Epidemiology -- Research -- Methodology
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Spatial analysis (Statistics)
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| Form |
Electronic book
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| Author |
Zidek, James V
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| ISBN |
9781482237047 |
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1482237040 |
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9780429162947 |
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0429162944 |
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