Bayesian disease mapping: hierarchical modeling in spatial epidemiology
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Fla. [u.a.]
CRC Press
2009
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| Series: | Interdisciplinary statistics series
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XVII, 344 S. Ill., graph. Darst., Kt. |
| ISBN: | 9781584888406 1584888407 |
Staff View
MARC
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| 100 | 1 | |a Lawson, Andrew |d 1959- |e Verfasser |0 (DE-588)130212261 |4 aut | |
| 245 | 1 | 0 | |a Bayesian disease mapping |b hierarchical modeling in spatial epidemiology |c Andrew B. Lawson |
| 264 | 1 | |a Boca Raton, Fla. [u.a.] |b CRC Press |c 2009 | |
| 300 | |a XVII, 344 S. |b Ill., graph. Darst., Kt. | ||
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| 650 | 4 | |a Bayes Theorem | |
| 650 | 4 | |a Bayesian statistical decision theory | |
| 650 | 4 | |a Epidemiologic Methods | |
| 650 | 4 | |a Epidemiology |x Statistical methods | |
| 650 | 4 | |a Medical mapping | |
| 650 | 4 | |a Statistics as Topic | |
| 650 | 4 | |a Topography, Medical |x methods | |
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Record in the Search Index
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| adam_text | 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 Likelihood Models 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 Posterior Distributions 24
2.3.1 Conjugacy 25
2.3.2 Prior Choice 25
2.4 Predictive Distributions 26
2.4.1 Poisson-Gamma Example 26
2.5 Bayesian Hierarchical Modeling 27
2.6 Hierarchical Models 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 Markov Chain Monte Carlo Methods 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 versus Gibbs Algorithms 39
3.3.5 Special Methods 40
vii
viii Contents
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 GOF Measures 55
4.1.1 Deviance Information Criterion 56
4.1.2 Posterior Predictive Loss 57
4.2 General Residuals 59
4.3 Bayesian Residuals 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
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 Process Model 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 Linear Model 79
5.3 Simple Case and Count Data Models with Uncorrelated
Random Effects 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 Convolution Models 91
5.6 Model Comparison and Goodness-of-Fit Diagnostics 92
5.6.1 Residual Spatial Autocorrelation 94
5.7 Alternative Risk Models 96
5.7.1 Autologistic Models 96
5.7.2 Spline-Based Models 101
5.7.3 Zip Regression Models 102
5.7.4 Ordered and Unordered Multicategory Data 107
Contents ix
5.7.5 Latent Structure Models 108
5.8 Edge Effects Ill
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 Maximum Likelihood 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 Denned 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 Posterior Measures 130
6.4 Cluster Models 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 and Wombling 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 Hazard Models 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 Outcome Misalignment 198
8.2.3 Misalignment and Edge Effects 200
x Contents
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
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 Cox Model 232
10.2.6 Extensions 233
10.3 Spatial Longitudinal Analysis 234
10.3.1 General Model 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 Event Data 255
11.2 Count Data 257
11.2.1 Georgia Low Birth Weight Example . 262
11.3 Alternative Models 266
11.3.1 Autologistic Models 266
11.3.2 Latent Structure ST Models 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
Contents xi
A Basic R and WinBUGS 283
A.I Basic R Usage 283
A.I.I Data 283
A.1.2 Graphics 284
A.2 Use of R in Bayesian Modeling 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.I 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.ll 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
List of Tables
5.1 Comparison of convolution and uncorrelataed heterogeneity
models for the Georgia oral cancer dataset 93
5.2 DICs for three models: autologistic with no randon effects; aut-
logistic with UH component; convolution model with UH and
CH 98
7.1 Model fitting results for a variety of models for the South Car¬
olina congenital mortality data, (See text for details) 154
7.2 Results for a variety of models fitted to 1988 respiratory cancer
incident counts for counties of Ohio 175
7.3 Ohio respiratory cancer (1979-1988): putative source model fits 181
8.1 Goodness of fit results for separate and joint models for Georgia
oral cancer PH-county level data 190
8.2 Model fit results for the point misalignment example 197
9.1 Model comparisons for the three disease examples: joint, com¬
mon, and shared 207
9.2 Correlation of spatial random effects under the MCAR model
for the Georgia chronic disease example: upper triangle: corre¬
lation of the spatially structured effects; lower triangle: corre¬
lation of the relative risks 225
10.1 Comparison of four models for the seizure data: basic and Mod¬
els 1-3 239
10.2 Posterior average estimates of the model parameters under the
four different seizure models 240
10.3 Parameter estimates for the basic model compared to the best
fitting model (Model 1) 240
11.1 Space—time models for the Georgia oral cancer dataset; models
are explained in text 264
11.2 Autologistic space-time models: models 1-4; convolution model
(Model 5) 267
xiii
|
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| id | DE-604.BV035682530 |
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| indexdate | 2024-07-09T21:43:19Z |
| institution | BVB |
| isbn | 9781584888406 1584888407 |
| language | English |
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| spelling | Lawson, Andrew 1959- Verfasser (DE-588)130212261 aut Bayesian disease mapping hierarchical modeling in spatial epidemiology Andrew B. Lawson Boca Raton, Fla. [u.a.] CRC Press 2009 XVII, 344 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Interdisciplinary statistics series Epidemiologic Methods Bayes Theorem Statistics as Topic Topography, Medical methods Bayesian statistical decision theory Epidemiology Statistical methods Medical mapping Epidemiologie (DE-588)4015016-1 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd rswk-swf Epidemiologie (DE-588)4015016-1 s Medizinische Statistik (DE-588)4127563-9 s Bayes-Entscheidungstheorie (DE-588)4144220-9 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736783&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lawson, Andrew 1959- Bayesian disease mapping hierarchical modeling in spatial epidemiology Epidemiologic Methods Bayes Theorem Statistics as Topic Topography, Medical methods Bayesian statistical decision theory Epidemiology Statistical methods Medical mapping Epidemiologie (DE-588)4015016-1 gnd Medizinische Statistik (DE-588)4127563-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| subject_GND | (DE-588)4015016-1 (DE-588)4127563-9 (DE-588)4144220-9 |
| title | Bayesian disease mapping hierarchical modeling in spatial epidemiology |
| title_auth | Bayesian disease mapping hierarchical modeling in spatial epidemiology |
| title_exact_search | Bayesian disease mapping hierarchical modeling in spatial epidemiology |
| title_full | Bayesian disease mapping hierarchical modeling in spatial epidemiology Andrew B. Lawson |
| title_fullStr | Bayesian disease mapping hierarchical modeling in spatial epidemiology Andrew B. Lawson |
| title_full_unstemmed | Bayesian disease mapping hierarchical modeling in spatial epidemiology Andrew B. Lawson |
| title_short | Bayesian disease mapping |
| title_sort | bayesian disease mapping hierarchical modeling in spatial epidemiology |
| title_sub | hierarchical modeling in spatial epidemiology |
| topic | Epidemiologic Methods Bayes Theorem Statistics as Topic Topography, Medical methods Bayesian statistical decision theory Epidemiology Statistical methods Medical mapping Epidemiologie (DE-588)4015016-1 gnd Medizinische Statistik (DE-588)4127563-9 gnd Bayes-Entscheidungstheorie (DE-588)4144220-9 gnd |
| topic_facet | Epidemiologic Methods Bayes Theorem Statistics as Topic Topography, Medical methods Bayesian statistical decision theory Epidemiology Statistical methods Medical mapping Epidemiologie Medizinische Statistik Bayes-Entscheidungstheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017736783&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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