Muutke küpsiste eelistusi

Integrative Metaregression Framework for Descriptive Epidemiology [Kõva köide]

Edited by , Edited by , Edited by
Teised raamatud teemal:
Integrative Metaregression Framework for Descriptive EpidemiologySuurem pilt
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780295991849.html
Märksõnad:
To provide the tools and knowledge needed in efforts to improve the health of the world's populations, researchers collaborated on the Global Burden of Diseases, Injuries, and Risk Factors Study 2010. The study produced comprehensive estimates of over 200 diseases and health risk factors in 187 countries over two decades, results that will be used by governments and non-governmental agencies to inform priorities for global health research, policies, and funding.

Integrated Meta-Regression Framework for Descriptive Epidemiology is the first book-length treatment of model-based meta-analytic methods for descriptive epidemiology used in the Global Burden of Disease Study 2010. In addition to collecting the prior work on compartmental modeling of disease, this book significantly extends the model, by formally connecting the system dynamics model of disease progression to a statistical model of epidemiological rates and demonstrates how the two models were combined to allow researchers to integrate relevant data. Practical applications of the model to meta-analysis of more than a dozen different diseases complement the theoretical foundations of the integrative systems modeling of disease in populations. The book concludes with a detailed description of the future directions for research in model-based meta-analysis of descriptive epidemiological data.

Abraham Flaxman is assistant professor of global health in the Institute for Health Metrics and Evaluation at the University of Washington.

Muu info

First book-length treatment of model-based meta-analytic methods for descriptive epidemiology
Figures x
Tables xv
Acknowledgments xvii
Introduction xix
Abraham D. Flaxman
Theo Vos
Christopher J.L. Murray
An introductory example
xxiii
A motivating example
xxiv
From systematic review to metaregression
xxxi
History of generic disease modeling
xxxiv
What is not in this book
xxxvii
I Theory and methods
1 Background material on Bayesian methods
3(8)
Abraham D. Flaxman
1.1 A meta-analysis example
4(5)
1.2 Another meta-analysis example
9(1)
1.3 Summary
10(1)
2 Statistical models for rates, ratios, and durations
11(22)
Abraham D. Flaxman
2.1 A motivating example
13(1)
2.2 Binomial model
13(5)
2.3 Beta-binomial model
18(1)
2.4 Poisson model
19(2)
2.5 Negative-binomial mode
21(2)
2.6 Transformed normal models
23(2)
2.7 Lower-bound data model
25(2)
2.8 Quantification of uncertainty
27(2)
2.9 Comparison
29(2)
2.10 Summary and future work
31(2)
3 Age pattern models
33(10)
Abraham D. Flaxman
3.1 Definition of spline models
35(1)
3.2 Choosing knots
36(2)
3.3 Penalized spline models
38(1)
3.4 Augmenting the spline model
38(2)
3.5 Summary and future work
40(3)
4 Expert priors on age patterns
43(8)
Abraham D. Flaxman
4.1 Priors on level
44(2)
4.2 Priors on monotonicity
46(1)
4.3 Priors are not just for splines
47(1)
4.4 Hierarchical similarity priors on age patterns
48(1)
4.5 Summary and future work
49(2)
5 Statistical models for heterogeneous age groups
51(14)
Abraham D. Flaxman
5.1 Overlapping age-group data
53(2)
5.2 Midpoint model
55(2)
5.3 Disaggregation model
57(1)
5.4 Midpoint model with group width covariate
57(2)
5.5 Age-standardizing and age-integrating models
59(2)
5.6 Model comparison
61(2)
5.7 Summary and future work
63(2)
6 Covariate modeling
65(12)
Abraham D. Flaxman
6.1 Cross-walk fixed effects to explain bias
67(4)
6.2 Predictive fixed effects to improve out-of-sample estimation
71(1)
6.3 Fixed effects to explain variance
71(1)
6.4 Random effects for spatial variation
72(2)
6.5 Covariates and consistency
74(1)
6.6 Summary and future work
75(2)
7 Prevalence estimates from other data types
77(14)
Abraham D. Flaxman
7.1 A motivating example
78(1)
7.2 System dynamics model of disease in a population
79(7)
7.3 Endemic equilibrium
86(1)
7.4 Forward simulation examples
86(2)
7.5 Summary and future work
88(3)
8 Numerical algorithms
91(18)
Abraham D. Flaxman
8.1 Markov chain Monte Carlo
93(3)
8.2 The Metropolis-Hastings step method
96(1)
8.3 The Adaptive Metropolis step method
97(2)
8.4 Convergence of the MCMC algorithm
99(2)
8.5 Initial values for MCMC
101(1)
8.6 A meta-analysis example
101(1)
8.7 Empirical Bayesian priors to borrow strength between regions
102(1)
8.8 Summary and future work
103(1)
8.9 Challenges and limitations
104
II Applications
9 Knot selection in spline models
109(8)
Yong Yi Lee
Theo Vos
Abraham D. Flaxman
Jed Blore
Louisa Degenhardt
10 Unclear age pattern, requiring expert priors
117(8)
Hannah M. Peterson
Yong Yi Lee
Theo Vos
Abraham D. Flaxman
11 Empirical priors
125(4)
David Chou
Hannah M. Peterson
Abraham D. Flaxman
Christopher J.L. Murray
Mohsen Naghavi
12 Overlapping, heterogeneous age groups
129(6)
Mohammad H. Forouzanfar
Abraham D. Flaxman
Hannah M. Peterson
Mohsen Naghavi
Sumeet Chugh
13 Dealing with geographical variation
135(6)
Abraham D. Flaxman
Khayriyyah Mohd Hanaah
Justina Groeger
Hannah M. Peterson
Steven T. Wiersma
14 Cross-walking with fixed effects
141(4)
Amanda Baxter
Jed Blore
Abraham D. Flaxman
Theo Vos
Harvey Whiteford
15 Improving out-of-sample prediction
145(4)
Ali Mokdad
Abraham D. Flaxman
Hannah M. Peterson
Christopher J.L. Murray
Mohsen Naghavi
16 Risk factors
149(6)
Stephen S. Lim
Hannah M. Peterson
Abraham D. Flaxman
17 The compartmental model
155(6)
Sarah K. Wulf
Abraham D. Flaxman
Mohsen Naghavi
Giuseppe Remuzzi
18 Knot selection in compartmental spline models
161(4)
Marita Cross
Damian Hoy
Theo Vos
Abraham D. Flaxman
Lyn March
19 Expert priors in compartmental models
165(6)
Alize Ferrari
Abraham D. Flaxman
Hannah M. Peterson
Theo Vos
Harvey Whiteford
20 Cause-specific mortality rates
171(4)
Theo Vos
Jed Blore
Abraham D. Flaxman
Hannah M. Peterson
Juergen Rehm
Conclusion 175(4)
Abraham D. Flaxman
Christopher J.L. Murray
Theo Vos
Appendix: GBD Study 2010 spatial hierarchy 179(12)
References 191(16)
Contributors 207(1)
About the editors 208(2)
Index 210
Abraham D. Flaxman is assistant professor of global health at the Institute for Health Metrics and Evaluation at the University of Washington. Theo Vos is professor of global health at the Institute for Health Metrics and Evaluation at the University of Washington. Christopher J. L. Murray is professor of global health and director of the Institute for Health Metrics and Evaluation at the University of Washington.