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Produktbild: Lawson, A: Bayesian Disease Mapping
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Lawson, A: Bayesian Disease Mapping Hierarchical Modeling in Spatial Epidemiology

Aus der Reihe Chapman & Hall/CRC Interdisciplinary Statistics

Fr. 122.00

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Beschreibung

Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial...

Beschreibung

Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets. New to the Second Edition * Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance * Expanded material on case event modeling and spatiotemporal analysis * New and updated examples * Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.

Zitat

Praise for the Previous Edition This book provides a technical grounding in spatial models while maintaining a strong grasp on applied epidemiological problems. ... A welcome effort is made to clarify concepts which might, in other texts, have been skimmed over in a rush to fit models. ... From the start, the concepts are illustrated with disease mapping examples, including R and WinBUGS code. ... The book has relatively few errors ... I recommend the book. It taught me new ideas and clarified existing ones. I shall continue to use it and I expect it to be useful for other statisticians with an interest in spatial analysis. -Journal of the Royal Statistical Society, Series A, April 2011 The readers who would like to get a big picture of hierarchical modeling in spatial epidemiology in a quick fashion will find this book very useful. This book covers a range of topics in hierarchical modeling for spatial epidemiological data and provides a practical, comprehensive, and up-to-date overview of the use of spatial statistics in epidemiology. ... useful for readers to track down the topics of interests and see the varieties of up-to-date modeling techniques in spatial epidemiology or, more generally, spatial binary or count data. The author also lists the reference following each method for further information. -Hongfei Li, Technometrics, November 2010 Lawson begins by building a solid Bayesian background ... The remaining seven chapters provide a thorough review of modeling relative risk ... Lawson provides well-written reviews of many topics and many aspects of those topics are covered in his reviews. The literature cited is huge and diverse, showing the current importance of the subjects covered. One can also gain hands-on training in analysis and visual presentations ... by following carefully the detailed introduction to R and WinBUGS given in the book. Many important data sets used in the book are available online... -International Statistical Review (2009), 77, 2 This book is an excellent reference for intermediate learners of Bayesian disease mapping ... many of the methodologies discussed in this book are applicable not only to spatial epidemiology but also to many other fields that utilize spatial data. -J. Law, Biometrics, June 2009

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

02.04.2013
Abbildungen

81 black & white illustrations, 20 black & white tables
Verlag Taylor & Francis

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

02.04.2013
Abbildungen

81 black & white illustrations, 20 black & white tables
Verlag Taylor & Francis
Seitenzahl

396
Maße (L/B/H)

23.5/16.3/2.8 cm
Gewicht

680 g
Auflage

2. Auflage
Sprache

Englisch
ISBN

978-1-4665-0481-3

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  • Produktbild: Lawson, A: Bayesian Disease Mapping
  • BACKGROUND Introduction Datasets 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 Perfect Sampling Posterior and Likelihood Approximations Exercises Residuals and Goodness-of-Fit Model GOF Measures General Residuals Bayesian Residuals Predictive Residuals and the Bootstrap Interpretation of Residuals in a Bayesian Setting Pseudo Bayes Factors and Marginal Predictive Likelihood Other Diagnostics 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 Regression and Ecological Analysis Basic Regression Modeling Missing Data Non-Linear Predictors Confounding and Multi-Colinearity Geographically Dependent Regression Variable Selection Ecological Analysis: The General Case of Regression Biases and Misclassification Error Putative Hazard Modeling Case Event Data Aggregated Count Data Spatiotemporal Effects 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 Disease Map Surveillance Surveillance Concepts Temporal Surveillance Spatial and Spatiotemporal Surveillance Appendices