(Ebook) Spatio Temporal Methods in Environmental Epidemiology 1st Edition by Gavin Shaddick, James V Zidek ISBN 0429162944 9780429162947

(Ebook) Spatio Temporal Methods in Environmental Epidemiology 1st Edition by Gavin Shaddick, James V Zidek ISBN 0429162944 9780429162947

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ISBN 10: 0429162944 
ISBN 13: 9780429162947
Author: Gavin Shaddick, James V Zidek

Spatio-Temporal Methods in Environmental Epidemiology with R, like its First Edition, explores the interface between environmental epidemiology and spatio-temporal modeling. It links recent developments in spatio-temporal theory with epidemiological applications. Drawing on real-life problems, it shows how recent advances in methodology can assess the health risks associated with environmental hazards. The book's clear guidelines enable the implementation of the methodology and estimation of risks in practice. New additions to the Second Edition include: a thorough exploration of the underlying concepts behind knowledge discovery through data; a new chapter on extracting information from data using R and the tidyverse; additional material on methods for Bayesian computation, including the use of NIMBLE and Stan; new methods for performing spatio-temporal analysis and an updated chapter containing further topics. Throughout the book there are new examples, and the presentation of R code for examples has been extended. Along with these additions, the book now has a GitHub site (https://spacetime-environ.github.io/stepi2) that contains data, code and further worked examples. Features: • Explores the interface between environmental epidemiology and spatio­-temporal modeling • Incorporates examples that show how spatio-temporal methodology can inform societal concerns about the effects of environmental hazards on health • Uses a Bayesian foundation on which to build an integrated approach to spatio-temporal modeling and environmental epidemiology • Discusses data analysis and topics such as data visualization, mapping, wrangling and analysis • Shows how to design networks for monitoring hazardous environmental processes and the ill effects of preferential sampling • Through the listing and application of code, shows the power of R, tidyverse, NIMBLE and Stan and other modern tools in performing complex data analysis and modeling Representing a continuing important direction in environmental epidemiology, this book – in full color throughout – underscores the increasing need to consider dependencies in both space and time when modeling epidemiological data. Readers will learn how to identify and model patterns in spatio-temporal data and how to exploit dependencies over space and time to reduce bias and inefficiency when estimating risks to health.

(Ebook) Spatio Temporal Methods in Environmental Epidemiology 1st Table of contents:

1 An overview of spatio-temporal epidemiology and knowledge discovery
1.1 Overview
1.2 Health-exposure models
1.2.1 Estimating risks
1.2.2 Dependencies over space and time
1.2.3 Contrasts
1.3 Examples of spatio-temporal epidemiological analyzes
1.4 Good spatio-temporal modeling approaches
1.5 Knowledge discovery: acquiring information to reduce uncertainty
1.6 Data collection: a brief history
1.6.1 The census
1.7 Discovering knowledge in data
1.8 When things are not quite as they seem!
1.9 The population
1.10 The process model
1.11 The process sampling model
1.12 The data model
1.13 Summary
2 An introduction to modeling health risks and impacts
2.1 Overview
2.2 Types of epidemiological study
2.3 Measures of risk
2.3.1 Relative risk
2.3.2 Population attributable risk
2.3.3 Odds ratios
2.3.4 Relationship between odds ratios and relative risk
2.3.5 Odds ratios in case-control studies
2.4 Standardized mortality ratios (SMR)
2.4.1 Rates and expected numbers
2.5 Generalized linear models
2.5.1 Likelihood
2.5.2 Quasi-likelihood
2.5.3 Likelihood ratio tests
2.5.4 Link functions and error distributions
2.5.5 Comparing models
2.6 Generalized additive models
2.6.1 Smoothers
2.6.2 Splines
2.6.3 Penalized splines
2.7 Poisson models for count data
2.7.1 Estimating SMRs
2.7.2 Over-dispersion
2.8 Estimating relative risks in relation to exposures
2.9 Modeling the cumulative effects of exposure
2.10 Logistic models for case-control studies
2.11 Summary
2.12 Exercises
3 The importance of uncertainty: assessment and quantification
3.1 Overview
3.2 The wider world of uncertainty
3.3 Quantitative uncertainty
3.3.1 Data uncertainty
3.3.2 Model uncertainty
3.4 Methods for assessing uncertainty
3.4.1 Sensitivity analysis
3.4.2 Taylor series expansion
3.4.3 Monte Carlo sampling
3.4.4 Bayesian modeling
3.5 Quantifying uncertainty
3.5.1 Variance
3.5.2 Entropy
3.5.3 Information and uncertainty
3.5.4 Decomposing uncertainty with entropy
3.6 Summary
3.7 Exercises
4 Extracting information from data
4.1 Overview
4.2 Using R to process and analyze data
4.2.1 Importing a dataset
4.3 Data wrangling and the Tidyverse
4.3.1 Simple manipulations using Tidyverse
4.4 Grouping and summarizing
4.5 Summarize
4.6 Visualizing data
4.7 Summary
4.8 Exercises
5 Embracing uncertainty: the Bayesian approach
5.1 Overview
5.2 Introduction to Bayesian inference
5.3 Exchangeability
5.4 Using the posterior for inference
5.5 Predictions
5.6 Transformations of parameters
5.6.1 Prior distributions
5.6.2 Likelihood
5.6.3 Posterior distributions
5.7 Prior formulation
5.7.1 Conjugate priors
5.7.2 Reference priors
5.7.3 Transformations
5.7.4 Jeffreys prior
5.7.5 Improper priors
5.7.6 Joint priors
5.7.7 Nuisance parameters
5.8 Summary
5.9 Exercises
6 Approaches to Bayesian computation
6.1 Overview
6.2 Analytical approximations
6.3 Markov Chain Monte Carlo (MCMC)
6.3.1 Metropolis-Hastings algorithm
6.3.2 Gibbs sampling
6.3.3 Hybrid Gibbs sampler
6.3.4 Block updating
6.3.5 Hamiltonian Monte Carlo
6.3.6 Checking convergence
6.4 Using samples for inference
6.5 NIMBLE
6.6 RStan
6.7 INLA
6.7.1 Latent Gaussian models
6.7.2 Integrated Laplace approximations
6.7.3 R-INLA
6.8 Summary
6.9 Exercises
7 Strategies for modeling
7.1 Overview
7.2 Contrasts
7.3 Hierarchical models
7.3.1 Cluster effects
7.4 Generalized linear mixed models
7.5 Bayesian hierarchical models
7.6 Linking exposure and health models
7.6.1 Two-stage approaches
7.6.2 Multiple imputation
7.7 Model selection and comparison
7.7.1 Effect of selection on properties of estimators
7.7.2 Stepwise selection procedures
7.7.3 Bayesian regularization
7.8 What about the p-value?
7.9 Comparison of models
7.9.1 Bayes factors
7.9.2 Deviance information criterion – DIC
7.9.3 Watanabe–Akaike information criterion – WAIC
7.10 Bayesian model averaging
7.10.1 Interpretation
7.11 Summary
7.12 Exercises
8 The challenges of working with real-world data
8.1 Overview
8.2 Missing values
8.2.1 Imputation
8.2.2 Regression method
8.2.3 MCMC method
8.3 Measurement error
8.3.1 Classical measurement error
8.3.2 Berkson measurement error
8.3.3 Attenuation and bias
8.3.4 Estimation
8.4 Preferential sampling
8.4.1 Detecting and mitigating the effects of preferential sampling
8.5 Summary
8.6 Exercises

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Tags: Gavin Shaddick, James V Zidek, Spatio, Temporal