Bayesian Methods in Health Economics

Bayesian Methods in Health Economics

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  • Provides an overview of Bayesian methods for cost-effectiveness analysis, and includes all necessary background on economics and Bayesian statistics
  • Presents three detailed case studies of the cost-effectiveness analysis of health care interventions
  • Includes several worked examples to guide through the process of health economic evaluation
  • Contains extensive coverage of the practice of making Bayesian analysis integrating software such as JAGS and R, specifically for the application of health economic analysis
  • Systematically describes the methodological issues related to the application of Bayesian inference and decision process in health economics
  • Designed as a reference for students and practitioners working in the field of health economic evaluations and medical statistics

The book is linked to code with which to replicate the examples, and an associated R package (BCEA) can be used in real applications to produce systematic health economic evaluations of Bayesian models.


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.

Table of Contents

Introduction to Health Economic Evaluation
Health economic evaluation
Cost components
Types of economic evaluations
Comparing health interventions

Introduction to Bayesian Inference
Subjective probability and Bayes theorem
Bayesian (parametric) modelling
Choosing prior distributions and Bayesian computation

Statistical Cost-Effectiveness Analysis
Decision theory and expected utility
Decision-making in health economics
Probabilistic sensitivity analysis to parameter uncertainty
Reporting the results of probabilistic sensitivity analysis
Probabilistic sensitivity analysis to structural uncertainty
Advanced issues in cost-effectiveness analysis

Bayesian Analysis in Practice
Software configuration
An Example of analysis in JAGS/BUGS
Logical nodes
For loops and node transformations
Predictive distributions
Modelling the cost-effectiveness of a new chemotherapy drug in R/JAGS

Health Economic Evaluation in Practice
Cost-effectiveness analysis alongside clinical trials
Evidence synthesis and hierarchical models
Markov models

Author Bio(s)

Editorial Reviews

"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

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