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E-raamat: Bayesian Methods in Health Economics

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(Department of Statistical Science, University College London, UK)
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Bayesian Methods in Health EconomicsSuurem pilt
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Baio (statistical science, U. College London) presents a textbook for an advanced course in statistical methods for health economics, for students with some knowledge of statistics. He develops models and examples using a combination of R and other statistical software for the main Bayesian analysis, which is usually based on Markov Chain Monte Carlo. After introducing health economics evaluation, he covers Bayesian inference, statistical cost-effectiveness analysis, Bayesian analysis in practice, and health economic evaluation in practice. Annotation ©2013 Book News, Inc., Portland, OR (booknews.com)

Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis and hierarchical models and Markov models. The text uses WinBUGS and JAGS with datasets and code available online.

Arvustused

"Gianluca Baios book is a welcome account of recent developments in methodology for cost-effective analysis in health care. The book may well be the first book-length account of a fully Bayesian approach to cost-effective analysis. a great book for its intended audience of students in an advanced course on statistical methods for health economics. The book would also be suitable for self-study for at least two groups: Bayesian statisticians moving into health economics applications; practicing health economists and epidemiologists keen to learn more about Bayesian methods." Australian & New Zealand Journal of Statistics, 57, 2015

"It is well presented and pleasing to read. One of the strengths of the book is the use of real practical motivating examples, which then serve as vehicles for explaining methods. As each story unfolds, the reader is presented with the right level of mathematical detail to appreciate the problem and the analysis, followed by a full description of the R and JAGS code necessary to replicate the analysis. All the code in the book is also available from the authors website, and the authors associated R package (BCEA) contains useful post-processing functions I would recommend the book to anyone engaged in mathematical modeling for health economic decision making. The book would be particularly useful either for someone who is familiar with R but not with Bayesian methods in health economics, or for an experienced modeler who wants to migrate to R from a different software package. It also would not be hard to use the book as the basis for either a short course on Bayesian methods for health economic modeling, or perhaps a masters-level module. a nice addition to the literature on health economics from a statistical perspective." Journal of the American Statistical Association, December 2014

"This book is apparently the first book devoted to Bayesian statistical methods in health economics, which is a relatively new discipline. suitable for researchers and practitioners who want to learn and apply statistical methods to health economics. Also it can be a good text for graduate courses in statistical analysis of health economic data. The author tries to keep mathematics at a low level and provides many interesting figures and tables for readers with weak mathematical/statistical background. He provides step-by-step guidance to practical application of the Bayesian methods by using popular statistical software R and BUGS/JAGS. This would be very attractive to practitioners for they can easily implement Monte Carlo simulation methods necessary for Bayesian inference without fear." Man-Suk Oh, Biometrics, March 2014

Preface xiii
Glossary xvii
1 Introduction to health economic evaluation
1(28)
1.1 Introduction
1(1)
1.2 Health economic evaluation
2(4)
1.2.1 Clinical trials versus decision-analytical models
5(1)
1.3 Cost components
6(3)
1.3.1 Perspective and what costs include
6(1)
1.3.2 Sources and types of cost data
7(2)
1.4 Outcomes
9(8)
1.4.1 Condition specific outcomes
10(1)
1.4.2 Generic outcomes
10(3)
1.4.3 Valuing outcomes
13(4)
1.5 Discounting
17(1)
1.6 Types of economic evaluations
18(7)
1.6.1 Cost-minimisation analysis
18(1)
1.6.2 Cost-benefit analysis
19(3)
1.6.3 Cost-effectiveness analysis
22(1)
1.6.4 Cost-utility analysis
22(3)
1.7 Comparing health interventions
25(4)
1.7.1 The cost-effectiveness plane
27(2)
2 Introduction to Bayesian inference
29(46)
2.1 Introduction
29(2)
2.2 Subjective probability and Bayes theorem
31(7)
2.2.1 Probability as a measure of uncertainty against a standard
31(2)
2.2.2 Fundamental rules of probability
33(1)
2.2.3 Coherence
34(2)
2.2.4 Bayes theorem
36(2)
2.3 Bayesian (parametric) modelling
38(10)
2.3.1 Exchangeability and predictive inference
40(3)
2.3.2 Inference on the posterior distribution
43(5)
2.4 Choosing prior distributions and Bayesian computation
48(27)
2.4.1 Vague priors
48(5)
2.4.2 Conjugate priors
53(5)
2.4.3 Monte Carlo estimation
58(3)
2.4.4 Nonconjugate priors
61(1)
2.4.5 Markov Chain Monte Carlo methods
62(3)
2.4.6 MCMC convergence
65(3)
2.4.7 MCMC autocorrelation
68(7)
3 Statistical cost-effectiveness analysis
75(40)
3.1 Introduction
75(1)
3.2 Decision theory and expected utility
76(4)
3.2.1 Problem
76(2)
3.2.2 Decision criterion: Maximisation of the expected utility
78(2)
3.3 Decision-making in health economics
80(11)
3.3.1 Statistical framework
81(2)
3.3.2 Decision process
83(1)
3.3.3 Choosing a utility function: The net benefit
84(5)
3.3.4 Uncertainty in the decision process
89(2)
3.4 Probabilistic sensitivity analysis to parameter uncertainty
91(1)
3.5 Reporting the results of probabilistic sensitivity analysis
92(10)
3.5.1 Cost-effectiveness acceptability curves
93(4)
3.5.2 The value of information
97(3)
3.5.3 The value of partial information
100(2)
3.6 Probabilistic sensitivity analysis to structural uncertainty
102(5)
3.7 Advanced issues in cost-effectiveness analysis
107(8)
3.7.1 Including a risk aversion parameter in the net benefit
107(3)
3.7.2 Expected value of information for mixed strategies
110(5)
4 Bayesian analysis in practice
115(38)
4.1 Introduction
115(1)
4.2 Software configuration
116(1)
4.3 An example of analysis in JAGS/BUGS
117(9)
4.3.1 Model specification
117(1)
4.3.2 Pre-processing in R
118(2)
4.3.3 Launching JAGS from R
120(2)
4.3.4 Checking convergence and post-processing in R
122(4)
4.4 Logical nodes
126(3)
4.5 For loops and node transformations
129(5)
4.5.1 Blocking to improve convergence
132(2)
4.6 Predictive distributions
134(6)
4.6.1 Predictive distributions as missing values
137(3)
4.7 Modelling the cost-effectiveness of a new chemotherapy drug in R/JAGS
140(13)
4.7.1 Programming the analysis of the EVPPI
145(3)
4.7.2 Programming probabilistic sensitivity analysis to structural uncertainty
148(5)
5 Health economic evaluation in practice
153(56)
5.1 Introduction
153(1)
5.2 Cost-effectiveness analysis alongside clinical trials
154(14)
5.2.1 Example: RCT of acupuncture for chronic headache in primary care
155(1)
5.2.2 Model description
155(2)
5.2.3 JAGS implementation
157(2)
5.2.4 Cost-effectiveness analysis
159(2)
5.2.5 Alternative specifications of the model
161(7)
5.3 Evidence synthesis and hierarchical models
168(12)
5.3.1 Example: Neuraminidase inhibitors to reduce influenza in healthy adults
172(1)
5.3.2 Model description
173(3)
5.3.3 JAGS implementation
176(3)
5.3.4 Cost-effectiveness analysis
179(1)
5.4 Markov models
180(29)
5.4.1 Example: Markov model for the treatment of asthma
185(3)
5.4.2 Model description
188(1)
5.4.3 JAGS implementation
189(2)
5.4.4 Cost-effectiveness analysis
191(4)
5.4.5 Adding memory to Markov models
195(2)
5.4.6 Indirect estimation of the transition probabilities
197(12)
References 209(14)
Index 223
Gianluca Baio
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