Bayesian Cost-Effectiveness Analysis with the R package BCEA - Baio, Gianluca;Berardi, Andrea;Heath, Anna
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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 standardized and highly…mehr

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Produktbeschreibung
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 standardized and highly customizable 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 standardize their workflow, for example in the preparation of dossiers in support ofmarketing authorization, or academic and scientific publications. This new edition includes a more streamlined description of examples and programming, focusing on the newest release of the BCEA package and recent work on Value of Information.
Autorenporträt
Gianluca Baio is a Professor of Statistics and Health Economics in the Department of Statistical Science at University College London (UK). Gianluca graduated in Statistics and Economics from the University of Florence (Italy). He then completed a PhD programme in Applied Statistics again at the University of Florence, after a period at the Program on the Pharmaceutical Industry at the MIT Sloan School of Management, Cambridge (USA); he then worked as a Research Fellow and then Lecturer in the Department of Statistical Science at University College London (UK). Gianluca's main interests are in Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems, hierarchical/multilevel models and causal inference using the decision-theoretic approach. Gianluca leads the Statistics for Health Economic Evaluation research group within the department of Statistical Science and was the co-director of UCL MSc Programme in Health Economics and Decision Science. His research activity is now (almost) officially dead, since he has become the head of the department of Statistical Science at UCL, in 2021. Andrea Berardi is a Vice President at Precision AQ with experience in conducting complex statistical and health economic analyses across several disease areas. Andrea designed and conducted analyses of clinical trial data, evidence synthesis analyses, built cost-effectiveness and budget impact models, and supported health technology assessment (HTA) submissions to European countries. Andrea is also an experienced designer and programmer of web applications for health economics, having designed and developed several web interfaces to economic models, statistical analyses, and market access tools using R/Shiny. Andrea graduated with an MSc in Biostatistics and Experimental Statistics with a focus on Bayesian methods in health economics from the University of Milan-Bicocca. Before joining Precision, Andrea was a Principal Consultant at PAREXEL, and before then he was the Health Economics Lead of the Evidence Assessment Group (EAG) at the British Medical Journal Technology Assessment Group (BMJ-TAG). Dr. Anna Heath is a Scientist at The Hospital for Sick Children, with affiliations at the University of Toronto and University College London. Her research aims to develop innovative statistical methods to design, prioritise and analyse clinical research within a Bayesian framework, with a focus on Value of Information methods. Dr. Nathan Green studied Mathematics and Statistics at the University of Newcastle-Upon-Tyne and completed a PhD in Applied Probability at the University of Liverpool. After several years working at the UK Ministry of Defence, where he applied novel Bayesian inference methods to real-world problems, he returned to academia in 2010 to focus on public health research. Dr Green is currently a Senior Research Fellow in the Department of Statistical Science at University College London, having previously worked across government and academic sectors on a wide range of topics, including oncology, tuberculosis, healthcare-associated infections, and sexually transmitted infections. His research interests span health economics, survival analysis, evidence synthesis, and epidemiology, with a particular emphasis on Bayesian statistical modelling. He is also an enthusiastic R programmer, with a focus on developing reproducible and innovative approaches to statistical analysis in health research.