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Bayesian Cost-Effectiveness Analysis with the R package BCEA 1st ed. 2017 [Mīkstie vāki]

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  • Formāts: Paperback / softback, 168 pages, height x width: 235x155 mm, weight: 2876 g, 26 Illustrations, color; 29 Illustrations, black and white; XVI, 168 p. 55 illus., 26 illus. in color., 1 Paperback / softback
  • Sērija : Use R!
  • Izdošanas datums: 07-Jun-2017
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319557165
  • ISBN-13: 9783319557168
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Bayesian Cost-Effectiveness Analysis with the R package BCEA 1st ed. 2017Palielināt
  • Formāts: Paperback / softback, 168 pages, height x width: 235x155 mm, weight: 2876 g, 26 Illustrations, color; 29 Illustrations, black and white; XVI, 168 p. 55 illus., 26 illus. in color., 1 Paperback / softback
  • Sērija : Use R!
  • Izdošanas datums: 07-Jun-2017
  • Izdevniecība: Springer International Publishing AG
  • ISBN-10: 3319557165
  • ISBN-13: 9783319557168
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783319557168.html
Keywords:

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented using practical examples and two running case studies. The book also describes in detail how to perform health economic evaluations using the R package BCEA (Bayesian Cost-Effectiveness Analysis). BCEA can be used to post-process the results of a Bayesian cost-effectiveness model and perform advanced analyses producing standardised and highly customisable outputs. It presents all the features of the package, including its many functions and their practical application, as well as its user-friendly web interface. The book is a valuable resource for statisticians and practitioners working in the field of health economics wanting to simplify and standardise their workflow, for example in the preparation of dossiers in support of marketing authorisation, or academic and scientific publications.

1 Bayesian Analysis in Health Economics
1(22)
1.1 Introduction
1(1)
1.2 Bayesian Inference and Computation
2(10)
1.2.1 Bayesian Ideas
2(1)
1.2.2 Specifying a Bayesian Model
3(7)
1.2.3 Bayesian Computation
10(2)
1.3 Basics of Health Economic Evaluation
12(4)
1.4 Doing Bayesian Analysis and Health Economic Evaluation in R
16(7)
1.4.1 Pre-processing the Data
17(1)
1.4.2 Building and Coding the Analysis Model
17(1)
1.4.3 Running the Model
18(1)
1.4.4 Post-processing the Results
19(1)
1.4.5 Performing the Decision Analysis
20(1)
1.4.6 Using BCEA
21(1)
References
21(2)
2 Case Studies
23(36)
2.1 Introduction
23(1)
2.2 Preliminaries: Computer Configuration
24(3)
2.2.1 MS Windows Users
25(1)
2.2.2 Linux or Mac OS Users
25(2)
2.3 Vaccine
27(16)
2.3.1 (Bayesian) Statistical Model
28(13)
2.3.2 Economic Model
41(2)
2.4 Smoking Cessation
43(16)
2.4.1 (Bayesian) Statistical Model
44(9)
2.4.2 Economic Model
53(4)
References
57(2)
3 BCEA---A R Package for Bayesian Cost-Effectiveness Analysis
59(34)
3.1 Introduction
59(4)
3.2 Economic Analysis: The bcea Function
63(8)
3.2.1 Example: Vaccine
65(3)
3.2.2 Example: Smoking
68(3)
3.3 Basic Health Economic Evaluation: The summary Command
71(3)
3.4 Cost-Effectiveness Plane
74(6)
3.4.1 The ceplane. plot Function
74(2)
3.4.2 Ggplot Version of the Cost-Effectiveness Plane
76(3)
3.4.3 Advanced Options for ceplane. plot
79(1)
3.5 Expected Incremental Benefit
80(4)
3.6 Contour Plots
84(5)
3.7 Health Economic Evaluation for Multiple Comparators and the Efficiency Frontier
89(4)
References
92(1)
4 Probabilistic Sensitivity Analysis Using BCEA
93(60)
4.1 Introduction
93(1)
4.2 Probabilistic Sensitivity Analysis for Parameter Uncertainty
94(15)
4.2.1 Summary Tables
97(2)
4.2.2 Cost-Effectiveness Acceptability Curve
99(10)
4.3 Value of Information Analysis
109(29)
4.3.1 Expected Value of Perfect Information
109(4)
4.3.2 Expected Value of Perfect Partial Information
113(2)
4.3.3 Approximation Methods for the Computation of the EVPPI
115(9)
4.3.4 Advanced Options
124(3)
4.3.5 Technical Options for Controlling the EVPPI Estimation Procedure
127(6)
4.3.6 Deprecated (Single-Parameter) Methods
133(2)
4.3.7 The Info-Rank Plot
135(3)
4.4 PSA Applied to Model Assumptions and Structural Uncertainty
138(15)
4.4.1 Mixed Strategy
138(3)
4.4.2 Including Risk Aversion in the Utility Function
141(1)
4.4.3 Probabilistic Sensitivity Analysis to Structural Uncertainty
142(9)
References
151(2)
5 BCEAweb: A User-Friendly Web-App to Use BCEA
153(14)
5.1 Introduction
153(1)
5.2 BCEAweb: A User-Friendly Web-App to Use BCEA
153(14)
5.2.1 A Brief Technical Overview of BCEAweb
154(1)
5.2.2 Note on Data Import
155(1)
5.2.3 Introduction to the Interface
155(2)
5.2.4 Check Assumptions
157(3)
5.2.5 Economic Analysis
160(2)
5.2.6 Probabilistic Sensitivity Analysis
162(2)
5.2.7 Value of Information
164(1)
5.2.8 Report
165(1)
References
166(1)
Index 167
Gianluca Baio graduated in Statistics and Economics from the University of Florence (Italy). After a period at the Program on the Pharmaceutical Industry at the MIT Sloan School of Management, Cambridge (USA), he completed a PhD programme in Applied Statistics, again at the University of Florence. He then worked as a research fellow and lecturer at the Department of Statistical Sciences at University College London (UK). Gianluca's main interests are in Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in health systems; hierarchical/multilevel models; and causal inference using the decision-theoretic approach. Gianluca leads the Statistics for Health Economic Evaluation research group at the Department of Statistical Science. Andrea Berardi graduated in Biostatistics and Experimental Statistics from the University of Milano-Bicocca (Italy) and is a senior consultant at the Health Economics Modelling Unit at PAREXEL. He has experience both as a consultant for world-leading pharmaceutical companies and as health economics lead of the critical appraisal of NICE submissions as part of the BMJ Technology Assessment Group. Andreas experience of conducting and reviewing health economics analyses spans numerous and diverse disease areas. His main interests are the analysis of uncertainty and survival in health economics modelling.





Anna Heath is a PhD student at the Department of Statistical Science at University College London. She is currently working on calculation methods for value of information measures in health economic evaluations. Her work on the expected value of partial perfect information (EVPPI) is integrated into BCEA.