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
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
An Example of analysis in JAGS/BUGS
For loops and node transformations
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
"Gianluca Baio’s 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 author’s website, and the author’s 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