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【下载】Bayesian Disease Mapping：Hierarchical Modeling in Spatial Epidemiology
kxjs2007
4889 13
2010-06-03
Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (Chapman & Hall/CRC Interdisciplinary Statistics) (Hardcover)
Andrew Lawson (Author)

Editorial Reviews


Product Description


Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology provides an overview of the main areas of Bayesian hierarchical modeling and its application to the geographical analysis of disease.


The book explores a range of topics in Bayesian inference and modeling, including Markov chain Monte Carlo methods, Gibbs sampling, the Metropolis–Hastings algorithm, goodness-of-fit measures, and residual diagnostics. It also focuses on special topics, such as cluster detection; space-time modeling; and multivariate, survival, and longitudinal analyses. The author explains how to apply these methods to disease mapping using numerous real-world data sets pertaining to cancer, asthma, epilepsy, foot and mouth disease, influenza, and other diseases. In the appendices, he shows how R and WinBUGS can be useful tools in data manipulation and simulation.


Applying Bayesian methods to the modeling of georeferenced health data, Bayesian Disease Mapping proves that the application of these approaches to biostatistical problems can yield important insights into data.


About the Author


University of South Carolina, Columbia, USA University of Kent, UK University of Copenhagen, Denmark Utrecht University, The Netherlands University of California, Berkeley, USA



Product Details


·   Hardcover: 368 pages


·   Publisher: Chapman and Hall/CRC; 1 edition (August 5, 2008)


·   Language: English


·   ISBN-10: 1584888407


·   ISBN-13: 978-1584888406

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kxjs2007
2010-6-3 07:33:55

Contents

List of Tables xiii

Preface xv

Author xvii

I Background 1

1 Introduction 3

1.1 Datasets 5

2 Bayesian Inference and Modeling 19

2.1 LikelihoodModels 19

2.1.1 Spatial Correlation 20

2.2 Prior Distributions 22

2.2.1 Propriety 22

2.2.2 Noninformative Priors 22

2.3 PosteriorDistributions 24

2.3.1 Conjugacy 25

2.3.2 Prior Choice 25

2.4 PredictiveDistributions 26

2.4.1 Poisson–GammaExample 26

2.5 BayesianHierarchicalModeling 27

2.6 HierarchicalModels 27

2.7 Posterior Inference 28

2.7.1 A Bernoulli and Binomial Example 30

2.8 Exercises 34

3 Computational Issues 35

3.1 Posterior Sampling 35

3.2 MarkovChainMonte CarloMethods 36

3.3 Metropolis and Metropolis–Hastings Algorithms 37

3.3.1 Metropolis Updates 38

3.3.2 Metropolis–Hastings Updates 38

3.3.3 Gibbs Updates 38

3.3.4 M–H versusGibbs Algorithms 39

3.3.5 SpecialMethods 40

3.3.6 Convergence 40

3.3.7 Subsampling and Thinning 45

3.4 Perfect Sampling 47

3.5 Posterior and Likelihood Approximations 48

3.5.1 Pseudolikelihood and Other Forms 48

3.5.2 Asymptotic Approximations 50

3.6 Exercises 53

4 Residuals and Goodness-of-Fit 55

4.1 Model GOFMeasures 55

4.1.1 Deviance Information Criterion 56

4.1.2 Posterior Predictive Loss 57

4.2 General Residuals 59

4.3 BayesianResiduals 61

4.4 Predictive Residuals and the Bootstrap 62

4.4.1 Conditional Predictive Ordinates 63

4.5 Interpretation of Residuals in a Bayesian Setting 64

4.6 Exceedence Probabilities 64

4.7 Exercises 67

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kxjs2007
2010-6-3 07:35:10

II Themes 71

5 Disease Map Reconstruction and Relative Risk Estimation 73

5.1 Introduction to Case Event and Count Likelihoods 73

5.1.1 Poisson ProcessModel 73

5.1.2 Conditional Logistic Model 75

5.1.3 Binomial Model for Count Data 76

5.1.4 Poisson Model for Count Data 76

5.2 Specification of the Predictor in Case Event and Count Models 77

5.2.1 Bayesian LinearModel 79

5.3 Simple Case and Count Data Models with Uncorrelated

RandomEffects 80

5.3.1 Gamma and Beta Models 82

5.3.2 Log-Normal/Logistic-Normal Models 84

5.4 Correlated Heterogeneity Models 84

5.4.1 Conditional Autoregressive (CAR) Models 86

5.4.2 Fully-Specified Covariance Models 90

5.5 ConvolutionModels 91

5.6 Model Comparison and Goodness-of-Fit Diagnostics 92

5.6.1 Residual Spatial Autocorrelation 94

5.7 Alternative RiskModels 96

5.7.1 AutologisticModels 96

5.7.2 Spline-BasedModels 101

5.7.3 Zip Regression Models 102

5.7.4 Ordered and Unordered Multicategory Data 107

5.7.5 Latent Structure Models 108

5.8 Edge Effects 111

5.8.1 Edge Weighting Schemes and McMC Methods 113

5.8.2 Discussion and Extension to Space–Time 115

5.9 Exercises 116

5.9.1 MaximumLikelihood 116

5.9.2 Poisson–Gamma Model: Posterior and Predictive

Inference 117

5.9.3 Poisson-Gamma Model: Empirical Bayes 117

6 Disease Cluster Detection 119

6.1 Cluster Definitions 119

6.1.1 Hot Spot Clustering 121

6.1.2 Clusters as Objects or Groupings 121

6.1.3 Clusters Defined as Residuals 121

6.2 Cluster Detection using Residuals 122

6.2.1 Case Event Data 122

6.2.2 Count Data 126

6.3 Cluster Detection Using PosteriorMeasures 130

6.4 ClusterModels 133

6.4.1 Case Event Data 133

6.4.2 Count Data 143

6.4.3 Markov Connected Component Field (MCCF) Models 148

6.5 Edge Detection andWombling 149

7 Ecological Analysis 151

7.1 General Case of Regression 151

7.2 Biases and Misclassification Error 158

7.2.1 Ecological Biases 158

7.3 Putative HazardModels 165

7.3.1 Case Event Data 166

7.3.2 Aggregated Count Data 172

7.3.3 Spatiotemporal Effects 176

8 Multiple Scale Analysis 185

8.1 Modifiable Areal Unit Problem(MAUP) 185

8.1.1 Scaling Up 185

8.1.2 Scaling Down 187

8.1.3 Multiscale Analysis 187

8.2 Misaligned Data Problem(MIDP) 190

8.2.1 Predictor Misalignment 191

8.2.2 OutcomeMisalignment 198

8.2.3 Misalignment and Edge Effects 200

9 Multivariate Disease Analysis 201

9.1 Notation for Multivariate Analysis 201

9.1.1 Case Event Data 201

9.1.2 Count Data 202

9.2 Two Diseases 202

9.2.1 Case Event Data 202

9.2.2 Count Data 204

9.2.3 Georgia County Level Example (3 Diseases) 206

9.3 Multiple Diseases 207

9.3.1 Case Event Data 209

9.3.2 Count Data 216

9.3.3 Multivariate Spatial Correlation and MCAR Models 219

9.3.4 Georgia Chronic Ambulatory Care-Sensitive

Example 222

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kxjs2007
2010-6-3 07:35:29
10 Spatial Survival and Longitudinal Analysis 227
10.1 General Issues 227
10.2 Spatial Survival Analysis 228
10.2.1 Endpoint Distributions 228
10.2.2 Censoring 229
10.2.3 Random Effect Specification 230
10.2.4 General Hazard Model 232
10.2.5 CoxModel 232
10.2.6 Extensions 233
10.3 Spatial Longitudinal Analysis 234
10.3.1 GeneralModel 237
10.3.2 Seizure Data Example 237
10.3.3 Missing Data 241
10.4 Extensions to Repeated Events 243
10.4.1 Simple Repeated Events 243
10.4.2 More Complex Repeated Events 244
10.4.3 Fixed Time Periods 247
11 Spatiotemporal Disease Mapping 255
11.1 Case EventData 255
11.2 Count Data 257
11.2.1 Georgia Low Birth Weight Example 262
11.3 AlternativeModels 266
11.3.1 Autologistic Models 266
11.3.2 Latent Structure STModels 268
11.4 Infectious Diseases 271
11.4.1 Case Event Data 272
11.4.2 Count Data 273
11.4.3 Special Case: Veterinary Disease Mapping 276
A Basic R and WinBUGS 283
A.1 Basic R Usage 283
A.1.1 Data 283
A.1.2 Graphics 284
A.2 Use of R in BayesianModeling 287
A.3 WinBUGS 290
A.3.1 Simulation 291
A.3.2 Model Code 291
A.4 R2WinBUGS Function 298
A.5 BRugs 302
A.6 Maps on R and GeoBUGS 305
B Selected WinBUGS Code 307
B.1 Code for the Convolution Model (Chapter 5) 307
B.2 Code for Spatial Spline Model (Chapter 5) 308
B.3 Code for the Spatial Autologistic Model (Chapter 6) 308
B.4 Code for Logistic Spatial Case Control Model (Chapter 6) 309
B.5 Code for PP Residual Model (Chapter 6) 309
B.5.1 Same Model with Uncorrelated Random Effect 310
B.6 Code for the Logistic Spatial Case-Control Model (Chapter 6) 310
B.7 Code for Poisson Residual Clustering Example (Chapter 6) 312
B.8 Code for the Proper CAR Model (Chapter 7) 312
B.9 Code for the Multiscale Model for PH and County Level Data
(Chapter 8) 313
B.10 Code for the Shared Component Model for Georgia Asthma
and COPD (Chapter 9) 314
B.11 Code for the Seizure Example with Spatial Effect (Chapter 10) 315
B.12 Code for the Knorr-Held Model for Space–Time Relative Risk
Estimation (Chapter 11) 316
B.13 Code for the Space–Time Autologistic Model (Chapter 11) 316
C R Code for Thematic Mapping 319
References 321
Index 339
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andywang
2010-6-3 08:17:24
谢谢楼主分享！！
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bf2006
2010-6-3 08:33:05
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