Item Details

Statistical Methods in Spatial Epidemiology

Andrew B. Lawson
Format
Book
Published
Chichester ; New York : John Wiley, c2001.
Language
English
Series
Wiley Series in Probability and Statistics
ISBN
0471975729 (alk. paper)
Contents
  • Part I Nature of Spatial Epidemiology
  • Chapter 1 Definitions, Terminology and Data Sets 3
  • 1.1 Map Hypotheses and Modelling Approaches 5
  • 1.2 Definitions and Data Examples 7
  • 1.3 Further Definitions 9
  • Chapter 2 Scales of Measurement and Data Availability 21
  • 2.1 Small Scale 22
  • 2.2 Large Scale 22
  • 2.3 Rate Dependence 22
  • 2.4 Data Quality and the Ecological Fallacy 22
  • 2.5 Edge Effects 24
  • Chapter 3 Geographical Representation and Mapping 27
  • 3.2 Maps and Mapping 27
  • 3.3 Statistical Accuracy 32
  • 3.4 Aggregation 32
  • 3.5 Mapping Issues Related to Aggregated Data 33
  • Chapter 4 Basic Models 37
  • 4.1 Sampling Considerations 37
  • 4.2 Likelihood-based and Bayesian Approaches 38
  • 4.3 Point Event Models 38
  • 4.4 Count Models 54
  • Chapter 5 Exploratory Approaches, Parametric Estimation and Inference 61
  • 5.1 Exploratory Methods 62
  • 5.2 Parameter Estimation 73
  • 5.3 Residual Diagnostics 88
  • 5.4 Hypothesis Testing 90
  • 5.5 Edge Effects 91
  • Part II Important Problems in Spatial Epidemiology
  • Chapter 6 Small Scale: Disease Clustering 103
  • 6.1 Definition of Clusters and Clustering 104
  • 6.2 Modelling Issues 106
  • 6.3 Hypothesis Testing for Clusters 109
  • 6.4 Space-Time Clustering 113
  • 6.5 A Cluster Modelling Example 117
  • Chapter 7 Small Scale: Putative Sources of Hazard 123
  • 7.2 Study Design 124
  • 7.3 Problems of Inference 127
  • 7.4 Modelling the Hazard Exposure Risk 132
  • 7.5 Models for Case Event Data 140
  • 7.6 A Case Event Example 145
  • 7.7 Models for Count Data 147
  • 7.8 A Count Data Example 150
  • 7.9 Other Directions 151
  • Chapter 8 Large Scale: Disease Mapping 163
  • 8.2 Simple Statistical Representation 163
  • 8.3 Basic Models 167
  • 8.4 Advanced Methods 174
  • 8.5 Approximate Methods 180
  • 8.6 Evaluation of Model Performance 180
  • 8.7 Hypothesis Testing in Disease Mapping 182
  • 8.8 Space-Time Disease Mapping 185
  • 8.9 Disease Mapping: A German Case Study 189
  • Chapter 9 Large Scale: Surveillance 197
  • 9.1 Process Control Methodology 198
  • 9.2 Spatial Monitoring 199
  • Chapter 10 Ecological Analysis 207
  • 10.2 Small-Scale Modelling Issues 211
  • 10.3 Large-Scale Modelling Issues 213
  • 10.4 A Simple Example: Sudden Infant Death in North Carolina 216
  • 10.5 A Case Study: Malaria and IDDM 219
  • Chapter 11 Infectious Disease Modelling 223
  • 11.2 Model Development 224
  • 11.3 Modelling Special Cases 225
  • 11.4 Cluster Function Specification 227
  • 11.5 Data Example 228
  • 11.6 Revised Model 232
  • Appendix A Monte Carlo Testing and Simulation Envelopes 235
  • A.1 Nuisance Parameters and Test Statistics 235
  • A.2 Monte Carlo Tests 236
  • A.3 Simulation Envelopes 237
  • Appendix B Markov Chain Monte Carlo Methods 239
  • B.2 Metropolis and Metropolis-Hastings algorithms 240
  • Appendix C Metropolis-Hastings Cluster Sampling 245
  • C.1 Birth-Death-Diffusion Algorithm 245
  • C.2 Prior Distributions 246
  • C.3 Metropolis-Hastings Reversible Jump Sampler 246
  • C.4 Posterior Ratios for Cluster Terms 248
  • D.1 Case Event Estimators 249
  • D.2 Tract Count Estimators 249
  • Appendix E Software 251.
Description
x, 277 p. : ill. ; 24 cm.
Notes
Includes bibliographical references (p. [255]-268) and index.
Technical Details
  • Access in Virgo Classic

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    g| Part I t| Nature of Spatial Epidemiology -- g| Chapter 1 t| Definitions, Terminology and Data Sets g| 3 -- g| 1.1 t| Map Hypotheses and Modelling Approaches g| 5 -- g| 1.2 t| Definitions and Data Examples g| 7 -- g| 1.3 t| Further Definitions g| 9 -- g| Chapter 2 t| Scales of Measurement and Data Availability g| 21 -- g| 2.1 t| Small Scale g| 22 -- g| 2.2 t| Large Scale g| 22 -- g| 2.3 t| Rate Dependence g| 22 -- g| 2.4 t| Data Quality and the Ecological Fallacy g| 22 -- g| 2.5 t| Edge Effects g| 24 -- g| Chapter 3 t| Geographical Representation and Mapping g| 27 -- g| 3.2 t| Maps and Mapping g| 27 -- g| 3.3 t| Statistical Accuracy g| 32 -- g| 3.4 t| Aggregation g| 32 -- g| 3.5 t| Mapping Issues Related to Aggregated Data g| 33 -- g| Chapter 4 t| Basic Models g| 37 -- g| 4.1 t| Sampling Considerations g| 37 -- g| 4.2 t| Likelihood-based and Bayesian Approaches g| 38 -- g| 4.3 t| Point Event Models g| 38 -- g| 4.4 t| Count Models g| 54 -- g| Chapter 5 t| Exploratory Approaches, Parametric Estimation and Inference g| 61 -- g| 5.1 t| Exploratory Methods g| 62 -- g| 5.2 t| Parameter Estimation g| 73 -- g| 5.3 t| Residual Diagnostics g| 88 -- g| 5.4 t| Hypothesis Testing g| 90 -- g| 5.5 t| Edge Effects g| 91 -- g| Part II t| Important Problems in Spatial Epidemiology -- g| Chapter 6 t| Small Scale: Disease Clustering g| 103 -- g| 6.1 t| Definition of Clusters and Clustering g| 104 -- g| 6.2 t| Modelling Issues g| 106 -- g| 6.3 t| Hypothesis Testing for Clusters g| 109 -- g| 6.4 t| Space-Time Clustering g| 113 -- g| 6.5 t| A Cluster Modelling Example g| 117 -- g| Chapter 7 t| Small Scale: Putative Sources of Hazard g| 123 -- g| 7.2 t| Study Design g| 124 -- g| 7.3 t| Problems of Inference g| 127 -- g| 7.4 t| Modelling the Hazard Exposure Risk g| 132 -- g| 7.5 t| Models for Case Event Data g| 140 -- g| 7.6 t| A Case Event Example g| 145 -- g| 7.7 t| Models for Count Data g| 147 -- g| 7.8 t| A Count Data Example g| 150 -- g| 7.9 t| Other Directions g| 151 -- g| Chapter 8 t| Large Scale: Disease Mapping g| 163 -- g| 8.2 t| Simple Statistical Representation g| 163 -- g| 8.3 t| Basic Models g| 167 -- g| 8.4 t| Advanced Methods g| 174 -- g| 8.5 t| Approximate Methods g| 180 -- g| 8.6 t| Evaluation of Model Performance g| 180 -- g| 8.7 t| Hypothesis Testing in Disease Mapping g| 182 -- g| 8.8 t| Space-Time Disease Mapping g| 185 -- g| 8.9 t| Disease Mapping: A German Case Study g| 189 -- g| Chapter 9 t| Large Scale: Surveillance g| 197 -- g| 9.1 t| Process Control Methodology g| 198 -- g| 9.2 t| Spatial Monitoring g| 199 -- g| Chapter 10 t| Ecological Analysis g| 207 -- g| 10.2 t| Small-Scale Modelling Issues g| 211 -- g| 10.3 t| Large-Scale Modelling Issues g| 213 -- g| 10.4 t| A Simple Example: Sudden Infant Death in North Carolina g| 216 -- g| 10.5 t| A Case Study: Malaria and IDDM g| 219 -- g| Chapter 11 t| Infectious Disease Modelling g| 223 -- g| 11.2 t| Model Development g| 224 -- g| 11.3 t| Modelling Special Cases g| 225 -- g| 11.4 t| Cluster Function Specification g| 227 -- g| 11.5 t| Data Example g| 228 -- g| 11.6 t| Revised Model g| 232 -- g| Appendix A t| Monte Carlo Testing and Simulation Envelopes g| 235 -- g| A.1 t| Nuisance Parameters and Test Statistics g| 235 -- g| A.2 t| Monte Carlo Tests g| 236 -- g| A.3 t| Simulation Envelopes g| 237 -- g| Appendix B t| Markov Chain Monte Carlo Methods g| 239 -- g| B.2 t| Metropolis and Metropolis-Hastings algorithms g| 240 -- g| Appendix C t| Metropolis-Hastings Cluster Sampling g| 245 -- g| C.1 t| Birth-Death-Diffusion Algorithm g| 245 -- g| C.2 t| Prior Distributions g| 246 -- g| C.3 t| Metropolis-Hastings Reversible Jump Sampler g| 246 -- g| C.4 t| Posterior Ratios for Cluster Terms g| 248 -- g| D.1 t| Case Event Estimators g| 249 -- g| D.2 t| Tract Count Estimators g| 249 -- g| Appendix E t| Software g| 251.
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