Disease modelling and public health, part A:
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
North Holland
[2017]
|
| Schriftenreihe: | Handbook of statistics
volume 36 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xviii, 493 Seiten Illustrationen, Diagramme 24 cm |
| ISBN: | 9780444639684 |
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| 245 | 1 | 0 | |a Disease modelling and public health, part A |c edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| 264 | 1 | |a Amsterdam |b North Holland |c [2017] | |
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Datensatz im Suchindex
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Contents
Contributors xv
Preface xvii
Section I
Introduction and Disease Modeling
1. Fundamentals of Mathematical Models of Infectious
Diseases and Their Application to Data Analyses 3
Masayuki Kakehashi and Shoko Kawano
1 Introduction: Fundamentals of Infectious Disease Dynamical
Models 4
1.1 Population Dynamics of Biological Populations 5
1.2 Infectious Disease Spread Models, or Theoretical
Epidemiology 6
1.3 Important Concepts in Infectious Disease Epidemiology 11
1.4 Important Concepts From Dynamical Models of Infectious
Diseases 12
2 Analyses of Whole Population: Macroscopic Analyses 15
2.1 Data Description 15
2.2 Simple Regression Analysis 17
2.3 The Effect of School Closure 22
2.4 Incorporating Exposed Phase: SEfR Model 24
2.5 Distributions of Latent and Infectious Periods 25
2.6 Multiple Subgroups and Generation Matrix 31
3 Stochastic Model of Infectious Disease Spread: Microscopic
Model Considering Each Class 33
3.1 Analyses for Counted Data 34
3.2 Modeling the Reporting Delay 35
3.3 Modeling the Transition of Infectious Diseases 36
3.4 Reconstruction of the Values of State Variables of the System 38
3.5 Analysis and Simulation, and the Validity of the Model 39
4 An Analysis of Spatial Distribution 40
4.1 Location of Schools 41
4.2 Estimating Transition Kernel 41
4.3 Influence of the Network of Transmission 43
v
vi
Contents
5 Conclusion 43
References 44
Further Reading 45
2. Dynamic Risk Prediction for Cardiovascular Disease:
An Illustration Using the ARIC Study 47
Jessica K. Barrett, Michael J. Sweeting, and Angela M.
Wood
1 Introduction 47
2 Landmarking 49
2.1 The Landmarking Method 50
2.2 Dynamic Prediction 52
3 joint Models 53
3.1 Model Specification 53
3.2 Estimation 54
3.3 Dynamic Prediction 56
4 Assessing Predictive Performance 56
4.1 Area Under the Receiver Operating Characteristic Curve 57
4.2 Brier Score 58
5 Example: The ARIC Study 58
6 Discussion 62
Acknowledgments 63
References 64
3. Statistical Models for Selected Infectious Diseases 67
Poduri S.R.S. Rao
1 Common Cold and Asthma Exacerbation 67
2 Influenza 68
2.1 Surveillance and Estimates of the Center for Disease
Control and Prevention 68
2.2 Influenza and Respiratory Syncytial Virus in the
United States 68
2.3 Measles and Influenza Outbreaks 69
2.4 SIRS and Hierarchical Bayesian Models 69
2.5 Autoregressive and Bayesian Models for the Spread
of Influenza 70
2.6 Correlation of Surveillance Systems and Information
Environment 70
2.7 The Delphi System 70
3 Tuberculosis 70
3.1 Statistical Models for TB Incidence, Prevalence,
and Mortality Estimates 70
3.2 Mathematical Models 71
3.3 Regression and Bayesian Models 71
3.4 Statistical Relational Models for Structured
Epidemiological Characteristics 72
Contents
• »
VII
3.5 Bayesian Analysis for the Prevalence of TB 72
3.6 Partial Least Squares and Weighted Regression for the
Factors Affecting TB 72
3.7 Mathematical Models for the Resistance and Mechanism
of TB and Its Relapse 72
4 Malaria 73
References 73
Further Reading 74
4. Finite Mixture Models in Biostatistics 75
Sharon X. Lee, Shu~Kay Ng, and Geoffrey J. McLachlan
1 Introduction 75
2 Finite Mixture Models 76
3 Robust Mixture Models 78
4 Analysis of Cytometric Data 80
4.1 Automated Gating of Single Sample 80
4.2 Clustering and Alignment of Cell Populations Across
Multiple Samples 81
4.3 Class Prediction for New Samples 84
5 Analysis of Gene Expression Data 89
5.1 Clustering of Gene Expression Data 90
5.2 Ranking of Correlated Genes 91
5.3 Controlling for FDR 96
6 Conclusions 98
References 99
Section II
Methods for Public Health Data
5. Alternative Sampling Designs for Time-to-Event
Data With Applications to Biomarker Discovery
in Alzheimer's Disease 105
Michelle M. Nuho and Daniel L Gillen
1 Introduction 106
2 A Brief Review of Survival Analysis 107
2.1 Censoring 107
2.2 Statistical Functions of Interest in Time-to-Event Data 109
2.3 Parametric Estimation of the Survival Distribution 110
2.4 Nonparametric Estimation of the Survival Distribution 111
3 Cox Proportional Hazards Model 116
4 Influence of Cases and Controls in the Cox Model 119
4.1 The Partial information in the Two-Sample Case 119
4.2 An Empirical Assessment of the Influence of Cases
and Controls 120
viii
Contents
5 Nested Case-Control Study 121
5.1 Introduction to the Nested Case—Control Design 121
5.2 Equivalence of the Cox Proportional Hazards and
Conditional Logistic Regression Model Under the
Nested Case—Control Design 124
5.3 Nested Case-Control Sampling Schemes 125
5.4 Software Implementation of the Standard Nested
Case—Control Design 129
5.5 Simulated Performance of the Nested Case—Control
Design 129
6 Case-Cohort Design 133
6.1 Introduction to the Case—Cohort Design 133
6.2 Implementation of the Case—Cohort Design 135
6.3 Software Implementation of the Case—Cohort Design 139
6.4 Simulated Performance of the Case-Cohort Design 139
7 Implementation of Sampling Designs Using Data
From the Alzheimer's Disease Neuroimaging
Initiative (ADNI) 142
8 Explicit Adjustment for Confounding Variables Using
Alternative Sampling Designs 148
8.1 Matching in the Nested Case—Control Design 149
8.2 The Exposure Stratified Case—Cohort Design 150
9 Nested Case-Control Design vs the Case-Cohort Design 152
9.1 Scientific Considerations 152
9.2 Statistical Considerations 153
10 Study Design 154
11 Discussion 156
Acknowledgments 157
Appendix 157
A.1 Implementing ADNI Analysis in R, SAS,
and STATA 157
A.2 Implementation of the ADNI Analysis Using R 158
A.3 Implementation in STATA 162
A.4 Implementation in SAS 164
References 165
6. Real-Time Estimation of the Case Fatality Ratio
and Risk Factors of Death 1 67
Hiroshi Nishiura
1 Introduction 167
2 Right Censoring: Core Issue of Real-Time Estimation 168
3 Right Censoring and Identification of Death
Risk Factors 170
4 Extensions and Future Challenges 1 71
Acknowledgments 173
References 173
Contents ix
7. Nonparametric Regression of State Occupation
Probabilities in a Multistate Model 1 75
Sutirtha Chakraborty, Somnath Datta, and Susmita Datta
1 Introduction 175
2 The Proposed Methodology 1 77
2.1 Data Structure and Notations 177
2.2 Additive Models 178
2.3 Conditional Transition Hazard Rates and State
Occupation Probabilities 180
2.4 Censoring Hazards and Estimation of the Weights K,{t) 181
3 Simulations 182
3.1 The Simulation Design 182
3.2 Conditionally Semi-Markov Network 183
3.3 Conditionally Markov Network 184
3.4 Study of the Censoring Bias 184
3.5 Study of Overall Estimation Error 186
3.6 Tests for Regression Effects and a Power Study 193
4 Application to Bone Marrow Transplant Data 194
5 Discussion 200
Acknowledgments 201
Appendix. (Proof of Theorem 1) 201
References 202
8. Gene Set Analysis: As Applied to Public Health
and Biomedical Studies 205
Shabnam Vatanpour and Irina Dinu
1 Introduction 205
1.1 What Are DNA Microarrays? 206
1.2 Challenges in the Analysis of DNA Microarray Studies 206
1.3 Why Gene Set Analysis? 208
2 Methods 210
2.1 Individual Gene Analysis Methods 210
2.2 Gene Set Analysis Methods 214
2.3 GSA Methods for Continuous Outcomes 215
3 An Application of GSA for Analysis of a Multivariate
Continuous Outcome 219
4 Discussion 220
References 224
9. Causal Inference in the Study of Infectious Disease 229
Bradley C. Saul, Michael G. Hudgens, and M, Elizabeth
Halloran
1 Introduction
2 Causal Assumptions
229
231
X
Contents
3 Causal Inference for Single and Multiple Point
Exposures 233
3.1 Time-Varying Exposures and the ^-Methods 233
4 Alternative Approaches to Address Confounding 236
4.1 Test-Negative Design 236
4.2 Negative Controls 237
4.3 Regression Discontinuity 238
5 Principal Stratification 239
5.1 Postinfection Selection 240
5.2 Principal Surrogates 241
6 Interference 241
7 Summary 243
References 243
Section III
Computing
10. Computational Modeling Approaches Linking
Health and Social Sciences: Sensitivity of Social
Determinants on the Patterns of Health Risk
Behaviors and Diseases 249
Anuj Mubayi
1 Introduction 250
1.1 Social and Contextual Influences 251
1.2 Ecological Models of Health Behavior 252
1.3 Modeling Methods for Health Behaviors in Literature 252
2 Quantitative Modeling Methods 255
2.1 Gathering Data (Survey; Ecological Momentary
Assessment) to Assess Ecological Complex Systems 255
2.2 Agent-Based Model 257
2.3 CART and Random Forests 261
2.4 Uncertainty and Sensitivity Analysis of a Function Using
CART and Random Forest 270
2.5 Text Mining of Twitter Data 273
3 Parameter Estimates and Sensitivity of a Dynamical
System Model Using Berkeley Madonna 279
3.1 Parameter Estimation of the Model 279
3.2 Local Parameter Sensitivity Analysis of the Model 282
Appendix. Codes 285
A.1 Example to Analyze Survey Data in R 285
A.2 NetLogo Code for ABM 285
A.3 SPARTAN Codes for Sensitivity Analysis of ABM 285
A.4 CART and Random Forests R Codes 285
A.5 Uncertainty and Sensitivity Analysis Using CART
in MATLAB® 291
Contents xi
A.6 R Code for Mining Twitter Data 296
A.7 Dynamical System Model in Berkeley Madonna 298
A.8 Data Sets 301
References 301
11. Data-Driven Computational Disease Spread
Modeling: From Measurement to Parametrization
and Control 305
Stefan Engb/om and Stefan Widgren
1 introduction 305
2 A Generic Data-Driven Epidemiological Framework 306
2.1 Continuous-Time Markov Chains 307
2.2 Concentration Variables 309
2.3 Spatio-Temporal Epidemic Networks 309
2.4 Discretization in Time 310
2.5 Simlnň An R Package for Data-Driven Stochastic
Disease Spread Simulations 312
3 Measurement Parametrization, and Control 315
3.1 A Running Example: The SIS£ Model 316
3.2 Equilibrium Behavior 317
3.3 Synthetic Feasibility Study of Parametrization 322
3.4 Exploring Options for Control 325
4 Conclusions 327
Acknowledgments 327
References 327
12. Individual and Collective Behavior in Public Health
Epidemiology 329
Jiangzhuo Chen, Bryan Lewis, Achla Marathe,
Madhav Marathe, Samarth Swarup,
and Anil K.S. Vullikanti
1 Introduction 329
1.1 Organization 331
2 Background 333
3 Qualitative/Verbal Models 335
4 Formal Models for Representing Behaviors 337
4.1 Computational Considerations 338
4.2 Game-Theoretic Models That Capture Strategic
Behavior 339
4.3 Markov Decision Process Models 342
4.4 Belief-Desire-Intention Model 344
5 Simulations to Study Coevolving Behaviors and Epidemics 345
5.1 Specification and Implementation of Behaviors in
Simulations 347
XII
Contents
6 Inferring Health Behaviors Using Real-World Data 349
6.1 Inferring Behavior Using Online and Offline Survey
Methods 350
6.2 Inferring Behaviors Using Social Media Data 350
6.3 Inferring Behaviors Using Crowdsourced Webapps 353
6.4 Mapping Behavioral Models on Synthetic Agents 353
7 Behavioral Interventions and Interactions 354
7.1 Behavioral Interactions 355
8 Case Studies 356
8.1 Distribution of Limited Antivirals During an Influenza
Pandemic 356
8.2 Primary Caregivers7 Behavior and Their Role
in Containing Secondary Transmission
Within Households 356
8.3 Friendship Networks, Social Norms, and Obesity 357
Acknowledgments 358
References 358
Section IV
Mathematical Modeling and Methods
13. Theoretical Advances in Type 2 Diabetes 369
Pranay Goel
1 Introduction 369
2 Causal Theories of Diabetes 371
3 Clinical Assessment of Diabetes 372
3.1 Open-Loop Approach: The Glucose Clamp
Technique 373
3.2 Closed-Loop Models of Glucose Tolerance 374
4 Mathematical Models of Glucose Intolerance 375
4.1 Bergman Minimal Model 375
4.2 The HOMA Model 376
4.3 Other Models 377
5 Life Course Models of Diabetes 377
5.1 The Topp Model 378
5.2 The Ha Model 379
5.3 The Hypersecretion Model 381
6 Obesity and Models of Weight Loss 382
6.1 The Hall Model of CR 382
6.2 Criticism of Energy Balance Models 385
6.3 Personalization of Nutrition 387
7 Data Science-Based Models 388
8 Further Reading and Future Directions 389
Acknowledgment 391
References 392
Contents
xiii
14. Helminth Dynamics: Mean Number of Worms,
Reproductive Rates 397
Arni S.R. Srinivasa Rao and Roy M. Anderson
1 Mean Number of Worms 397
1.1 Cross-Sectional Mean 398
1.2 Cohort Mean 399
1.3 Theorems on Worm Growth Potential in Hosts 400
2 Net Production Rates Within and Outside Human Host 401
3 Impact of Chemotherapy 402
4 Discussion 403
References 404
Section V
Bayesian Methods
15. Bayesian Methods in Public Health 407
Wesley O. Johnson, Elizabeth B. Ward, and Daniel L.
Gillen
1 Introduction 407
2 Comparing Proportions 417
2.1 Bayesian Inference for Cross-sectional or Cohort
Sampling 422
2.2 Bayesian Inference for Case-Control Sampling 425
3 Logistic Regression Modeling and Inference 427
4 Mixed/Hierarchical Modeling and Inference 436
5 Conclusions 441
Acknowledgment 441
References 441
16. Bayesian Disease Mapping for Public Health 443
Andrew Lawson and Duncan Lee
1 Introduction 443
2 Spatial Modeling 445
2.1 Data and Overall Model 445
2.2 Random Effect Models 446
2.3 Choice of Priors 448
2.4 Goodness of Fit and Variable Selection 448
3 Space-Time Modeling 449
4 Multivariate Modeling 452
4.1 Likelihood Models 452
4.2 Multivariate Spatial Correlation and MCAR Models 453
5 Software 455
5.1 General Purpose Software 456
5.2 Specialized Spatial Modeling Software 457
xiv Contents
6 Cluster Identification 458
7 Boundary Detection (Wombling) 460
8 Ecological Regression in Public Health 462
9 Disease Map Surveillance 463
9.1 Surveillance Concepts 464
9.2 Syndromic Surveillance 465
9.3 Process Control Ideas 465
9.4 Single Disease Sequence 466
9.5 Multiple Disease Sequences 466
9.6 Infectious Disease Surveillance 466
9.7 Spatial and Spatiotemporal Surveillance 467
10 Spatial Survival Analysis 469
10.1 Endpoint Distributions 469
10.2 Censoring 471
10.3 Random Effect Specification 471
10.4 General Hazard Model 472
11 Example 472
12 Discussion and Future Directions 475
Acknowledgments 475
References 475
Index 483 |
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| series | Handbook of statistics |
| series2 | Handbook of statistics |
| spelling | Disease modelling and public health, part A edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao Amsterdam North Holland [2017] © 2017 xviii, 493 Seiten Illustrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Handbook of statistics volume 36 Gesundheitswesen (DE-588)4020775-4 gnd rswk-swf Krankheit (DE-588)4032844-2 gnd rswk-swf Statistisches Modell (DE-588)4121722-6 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Krankheit (DE-588)4032844-2 s Gesundheitswesen (DE-588)4020775-4 s Statistisches Modell (DE-588)4121722-6 s DE-604 Rao, Arni S. R. Srinivasa (DE-588)1143256220 edt Pyne, Saumyadipta (DE-588)1120147824 edt Rao, Calyampudi Radhakrishna 1920-2023 (DE-588)119285924 edt Handbook of statistics volume 36 (DE-604)BV000002510 36 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029840910&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Disease modelling and public health, part A Handbook of statistics Gesundheitswesen (DE-588)4020775-4 gnd Krankheit (DE-588)4032844-2 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| subject_GND | (DE-588)4020775-4 (DE-588)4032844-2 (DE-588)4121722-6 (DE-588)4143413-4 |
| title | Disease modelling and public health, part A |
| title_auth | Disease modelling and public health, part A |
| title_exact_search | Disease modelling and public health, part A |
| title_full | Disease modelling and public health, part A edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| title_fullStr | Disease modelling and public health, part A edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| title_full_unstemmed | Disease modelling and public health, part A edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne, C.R. Rao |
| title_short | Disease modelling and public health, part A |
| title_sort | disease modelling and public health part a |
| topic | Gesundheitswesen (DE-588)4020775-4 gnd Krankheit (DE-588)4032844-2 gnd Statistisches Modell (DE-588)4121722-6 gnd |
| topic_facet | Gesundheitswesen Krankheit Statistisches Modell Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029840910&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV000002510 |
| work_keys_str_mv | AT raoarnisrsrinivasa diseasemodellingandpublichealthparta AT pynesaumyadipta diseasemodellingandpublichealthparta AT raocalyampudiradhakrishna diseasemodellingandpublichealthparta |