Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology.
As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy.
1. Section I: Introduction and Disease Modeling - p. xviii 2. Chapter 1: Fundamentals of Mathematical Models of Infectious Diseases and Their Application to Data Analyses - p. xviii 3. Chapter 2: Dynamic Risk Prediction for Cardiovascular Disease: An Illustration Using the ARIC Study - p. 45 4. Chapter 3: Statistical Models for Selected Infectious Diseases - p. 65 5. Chapter 4: Finite Mixture Models in Biostatistics - p. 74 6. Section II: Methods for Public Health Data - p. 102 7. Chapter 5: Alternative Sampling Designs for Time-to-Event Data With Applications to Biomarker Discovery in Alzheimer's Disease - p. 102 8. Abstract - p. 105 9. 1 Introduction - p. 106 10. 2 A Brief Review of Survival Analysis - p. 107 11. 3 Cox Proportional Hazards Model - p. 116 12. 4 Influence of Cases and Controls in the Cox Model - p. 119 13. 5 Nested Case–Control Study - p. 121 14. 6 Case–Cohort Design - p. 133 15. 7 Implementation of Sampling Designs Using Data From the Alzheimer's Disease Neuroimaging Initiative (ADNI) - p. 142 16. 8 Explicit Adjustment for Confounding Variables Using Alternative Sampling Designs - p. 148 17. 9 Nested Case–Control Design vs the Case–Cohort Design - p. 152 18. 10 Study Design - p. 154 19. 11 Discussion - p. 156 20. Acknowledgments - p. 157 21. Appendix - p. 157 22. Chapter 6: Real-Time Estimation of the Case Fatality Ratio and Risk Factors of Death - p. 166 23. Chapter 7: Nonparametric Regression of State Occupation Probabilities in a Multistate Model - p. 174 24. Chapter 8: Gene Set Analysis: As Applied to Public Health and Biomedical Studies - p. 203 25. Chapter 9: Causal Inference in the Study of Infectious Disease - p. 227 26. Section III: Computing - p. 246 27. Chapter 10: Computational Modeling Approaches Linking Health and Social Sciences: Sensitivity of Social Determinants on the Patterns of Health Risk Behaviors and Diseases - p. 246 28. Chapter 11: Data-Driven Computational Disease Spread Modeling: From Measurement to Parametrization and Control - p. 304 29. Chapter 12: Individual and Collective Behavior in Public Health Epidemiology - p. 328 30. Section IV: Mathematical Modeling and Methods - p. 365 31. Chapter 13: Theoretical Advances in Type 2 Diabetes - p. 365 32. Chapter 14: Helminth Dynamics: Mean Number of Worms, Reproductive Rates - p. 395 33. Section V: Bayesian Methods - p. 404 34. Chapter 15: Bayesian Methods in Public Health - p. 404 35. Chapter 16: Bayesian Disease Mapping for Public Health - p. 442 36. Index - p. 483
Disease Modelling and Public Health, Part A, Volume 36 addresses new challenges in existing and emerging diseases with a variety of comprehensive chapters that cover Infectious Disease Modeling, Bayesian Disease Mapping for Public Health, Real time estimation of the case fatality ratio and risk factor of death, Alternative Sampling Designs for Time-To-Event Data with Applications to Biomarker Discovery in Alzheimer's Disease, Dynamic risk prediction for cardiovascular disease: An illustration using the ARIC Study, Theoretical advances in type 2 diabetes, Finite Mixture Models in Biostatistics, and Models of Individual and Collective Behavior for Public Health Epidemiology.
As a two part volume, the series covers an extensive range of techniques in the field. It present a vital resource for statisticians who need to access a number of different methods for assessing epidemic spread in population, or in formulating public health policy.
1. Section I: Introduction and Disease Modeling - p. xviii 2. Chapter 1: Fundamentals of Mathematical Models of Infectious Diseases and Their Application to Data Analyses - p. xviii 3. Chapter 2: Dynamic Risk Prediction for Cardiovascular Disease: An Illustration Using the ARIC Study - p. 45 4. Chapter 3: Statistical Models for Selected Infectious Diseases - p. 65 5. Chapter 4: Finite Mixture Models in Biostatistics - p. 74 6. Section II: Methods for Public Health Data - p. 102 7. Chapter 5: Alternative Sampling Designs for Time-to-Event Data With Applications to Biomarker Discovery in Alzheimer's Disease - p. 102 8. Abstract - p. 105 9. 1 Introduction - p. 106 10. 2 A Brief Review of Survival Analysis - p. 107 11. 3 Cox Proportional Hazards Model - p. 116 12. 4 Influence of Cases and Controls in the Cox Model - p. 119 13. 5 Nested Case–Control Study - p. 121 14. 6 Case–Cohort Design - p. 133 15. 7 Implementation of Sampling Designs Using Data From the Alzheimer's Disease Neuroimaging Initiative (ADNI) - p. 142 16. 8 Explicit Adjustment for Confounding Variables Using Alternative Sampling Designs - p. 148 17. 9 Nested Case–Control Design vs the Case–Cohort Design - p. 152 18. 10 Study Design - p. 154 19. 11 Discussion - p. 156 20. Acknowledgments - p. 157 21. Appendix - p. 157 22. Chapter 6: Real-Time Estimation of the Case Fatality Ratio and Risk Factors of Death - p. 166 23. Chapter 7: Nonparametric Regression of State Occupation Probabilities in a Multistate Model - p. 174 24. Chapter 8: Gene Set Analysis: As Applied to Public Health and Biomedical Studies - p. 203 25. Chapter 9: Causal Inference in the Study of Infectious Disease - p. 227 26. Section III: Computing - p. 246 27. Chapter 10: Computational Modeling Approaches Linking Health and Social Sciences: Sensitivity of Social Determinants on the Patterns of Health Risk Behaviors and Diseases - p. 246 28. Chapter 11: Data-Driven Computational Disease Spread Modeling: From Measurement to Parametrization and Control - p. 304 29. Chapter 12: Individual and Collective Behavior in Public Health Epidemiology - p. 328 30. Section IV: Mathematical Modeling and Methods - p. 365 31. Chapter 13: Theoretical Advances in Type 2 Diabetes - p. 365 32. Chapter 14: Helminth Dynamics: Mean Number of Worms, Reproductive Rates - p. 395 33. Section V: Bayesian Methods - p. 404 34. Chapter 15: Bayesian Methods in Public Health - p. 404 35. Chapter 16: Bayesian Disease Mapping for Public Health - p. 442 36. Index - p. 483
