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.
Table of Content
Bayesian analysis in health economics.- Case studies.- BCEA — a R package for Bayesian
cost-effectiveness analysis.- Probabilistic sensitivity analysis using BCEA.- BCEAweb: a user-friendly web-app to use BCEA.
About the author
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 Ph D 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. Andrea’s 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 Ph D 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.
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