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.
