Bayesian Models in Health Technology Assessment

1. Auflage 2026
ISBN: 978-1-041-29738-3
Verlag: Chapman and Hall/CRC

Buch, Englisch, 384 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 853 g

Reihe: Chapman & Hall/CRC Biostatistics Series

Bayesian models in Health Technology Assessment aims at presenting a thorough and yet accessible description of the philosophy underlying the Bayesian approach to statistical inference, as specifically applied to the process of health technology assessment (HTA). The book is grounded in practical examples, mostly taken from the HTA context and covering a wide range of real problems, typically encountered by modellers in their day-to-day work. All the chapters present methodological details, as well as carefully curated R and JAGS code, which can be used as a template to unlock the potential of Bayesian modelling, specifically in HTA.

- Covers introductory chapters on Bayesian modelling and computation, as well as the basics of the statistical modelling for HTA.

- Discusses a range of modelling topics, including the analysis of individual and aggregated-level data, as well as survival analysis and evidence synthesis.

- Presents further, more advanced modelling tools (such as for missing data, population adjustment methods and Value of Information), which should be increasingly familiar for practitioners working in HTA and beyond.

The text is primarily for modellers and practitioners working in the HTA context, regulators and reviewers of reimbursement dossiers and cost-effectiveness analysis. More generally, it aims at drawing the attention of researchers whose background is firmly statistical onto the interesting and high impactful area of HTA. It complements a wide range of undergraduate and graduate programmes in health technology assessment, health and public health economics, as well as academic researchers in the field of statistical modelling for health technology assessment.

Zielgruppe

Academic, Postgraduate, and Professional Reference

Autoren/Hrsg.

Baio, Gianluca

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Epidemiologie, Medizinische Statistik
• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

1 Introduction to Bayesian reasoning. 2 Learning from data: Bayesian computation. 3 Bayesian software. 4 Introduction to health technology assessment. 5 Cost-effectiveness analysis with individual level data. 6 Aggregated level data and evidence synthesis. 7 Indirect treatment comparisons. 8 Survival analysis in HTA.9 Markov models. 10 Missing data and “structural values” in HTA. 11 Population adjustment.12 Value of information.

Über Autor(innen)

Gianluca Baio is Professor of Statistics and Health Economics in the Department of Statistical Science at University College London (UK). Prof. Baio'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. Prof. Baio 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. He is a founding member and former Scientific co-Director of the R-HTA consortium (https://r-hta.org/) and a founding member of the ConVOI (https://www.convoi-group.org/) network. He also served as Secretary (2014-2016) and then Programme Chair (2016-2018) in the Section on Biostatistics and Pharmaceutical Statistics of the International Society for Bayesian Analysis. He collaborates with the UK National Institute for Health and Care Excellence (NICE) as a Scientific Advisor on Health Technology Appraisal projects and has been the 18th Armitage Lecturer in November 2021. His research activity is now (almost) officially dead, since he has become the head of the Department of Statistical Science at UCL, in 2021.