Description
Book SynopsisFirst book-length treatment of model-based meta-analytic methods for descriptive epidemiology
Table of ContentsFigures
Tables
Acknowledgments
Introduction | by Abraham D. Flaxman, Theo Vos, and Christopher J.L. Murray
1. An Introductory Example
2. A Motivating Example
3. From Systematic Review to Metaregression
4. History of Generic Disease Modeling
5. What is Not in This Book
Section One | Theory and Methods
1. Background material on Bayesian methods / Abraham D. Flaxman
1.1 A meta-analysis example
1.2 Another meta-analysis example
1.3 Summary
2. Statistical models for rates, ratios, and durations / Abraham D. Flaxman
2.1 A motivating example
2.2 Binomial model
2.3 Beta-binomial model
2.4 Poisson model
2.5 Negative-binomial mode
2.6 Transformed normal models
2.7 Lower-bound data model
2.8 Quantification of uncertainty
2.9 Comparison
2.10 Summary and future work
3. Age pattern models / Abraham D. Flaxman
3.1 Definition of spline models
3.2 Choosing knots
3.3 Penalized spline models
3.4 Augmenting the spline model
3.5 Summary and future work
4. Expert priors on age patterns / Abraham D. Flaxman
4.1 Priors on level
4.2 Priors on monotonicity
4.3 Priors are not just for splines
4.4 Hierarchical similarity priors on age patterns
4.5 Summary and future work
5. Statistical models for heterogeneous age groups / Abraham D. Flaxman
5.1 Overlapping age-group data
5.2 Midpoint model
5.3 Disaggregation model
5.4 Midpoint model with group width covariate
5.5 Age-standardizing and age-integrating models
5.6 Model comparison
5.7 Summary and future work
6. Covariate modeling / Abraham D. Flaxman
6.1 Cross-walk fixed effects to explain bias
6.2 Predictive fixed effects to improve out-of-sample estimation
6.3 Fixed effects to explain variance
6.4 Random effects for spatial variation
6.5 Covariates and consistency
6.6 Summary and future work
7. Prevalence estimates from other data types / Abraham D. Flaxman
7.1 A motivating example
7.2 System dynamics model of disease in a population
7.3 Endemic equilibrium
7.4 Forward simulation examples
7.5 Summary and future work
8. Numerical algorithms / Abraham D. Flaxman
8.1 Markov chain Monte Carlo
8.2 The Metropolis-Hastings step method
8.3 The Adaptive Metropolis step method
8.4 Convergence of the MCMC algorithm
8.5 Initial values for MCMC
8.6 A meta-analysis example
8.7 Empirical Bayesian priors to borrow strength between regions
8.8 Summary and future work
8.9 Challenges and limitations
Section Two | Applications
9. Knot selection in spline models / Yong Yi Lee, Theo Vos, Abraham D. Flaxman, Jed Blore, and Louisa Degenhardt
10. Unclear age pattern, requiring expert priors / Hannah M. Peterson, Yong Yi Lee, Theo Vos, and Abraham D. Flaxman
11. Empirical priors / David Chou, Hannah M. Peterson, Abraham D. Flaxman, Christopher J.L. Murray, and Mohsen Naghavi
12. Overlapping, heterogeneous age groups / Mohammad H. Forouzanfar, Abraham D. Flaxman, Hannah M. Peterson, Mohsen Naghavi, and Sumeet Chugh
13. Dealing with geographical variation / Abraham D. Flaxman, Khayriyyah Mohd Hanaah, Justina Groeger, Hannah M. Peterson, and Steven T. Wiersma
14. Cross-walking with fixed effects / Amanda Baxter, Jed Blore, Abraham D. Flaxman, Theo Vos, and Harvey Whiteford
15. Improving out-of-sample prediction / Ali Mokdad, Abraham D. Flaxman, Hannah M. Peterson, Christopher J.L. Murray, and Mohsen Naghavi
16. Risk factors / Stephen S. Lim, Hannah M. Peterson, and Abraham D. Flaxman
17. The compartmental model / Sarah K. Wulf, Abraham D. Flaxman, Mohsen Naghavi, and Giuseppe Remuzzi
18. Knot selection in compartmental spline models / Marita Cross, Damian Hoy, Theo Vos, Abraham D. Flaxman, and Lyn March
19. Expert priors in compartmental models / Alize Ferrari, Abraham D. Flaxman, Hannah M. Peterson, Theo Vos, and Harvey Whiteford
20. Cause-specific mortality rates / Theo Vos, Jed Blore, Abraham D. Flaxman, Hannah M. Peterson, and Juergen Rehm
Conclusion / Abraham D. Flaxman, Christopher J.L. Murray, and Theo Vos
Appendix: GBD Study 2010 spatial hierarchy
References
Contributors
About the editors
Index
