Value of Information for Healthcare Decision-Making Chapman & Hall/CRC Biostatistics Series

Value of Information for Healthcare Decision-Makingintroduces the concept of Value of Information (VOI) use in health policy decision-making to determine the sensitivity of decisions to assumptions, and to prioritise and design future research. These methods, and their use in cost-effectiveness analysis, are increasingly acknowledged by health technology assessment authorities as vital.

Key Features:

• Provides a comprehensive overview of VOI
• Simplifies VOI
• Showcases state-of-the-art techniques for computing VOI
• Includes R statistical software package
• Provides results when using VOI methods
• Uses realistic decision model to illustrate key concepts

The primary audience for this book is health economic modellers and researchers, in industry, government, or academia, who wish to perform VOI analysis in health economic evaluations. It is relevant for postgraduate researchers and students in health economics or medical statistics who are required to learn the principles of VOI or undertake VOI analyses in their projects. The overall goal is to improve the understanding of these methods and make them easier to use.

1. Health Economic Modelling. 2 .A Case Study: A Novel Chemotherapy Treatment. 3. The Expected Value of Perfect or Partial Perfect Information. 4. The Expected Value of Sample Information. 5. Reporting and Presenting Value of Information Analyses. 6. Value of Information: Success Stories. 7. Value of Information for Estimation Instead of Decision Making. 8. Structural Uncertainty and Value of Information. 9. Rapid Value of Information using Minimal Modelling. 10. EVSI Portfolio Optimisation. 11. Assessing Value of Information Given Non-Optimal Previous Decisions. 12. Value of Information and Implementation.

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. Christopher Jackson is a Senior Statistician at the Medical Research Council Biostatistics Unit at the University of Cambridge. He works on developing statistical methods with applications to population health, in particular, methods for combining different sources of data, Bayesian methods, survival and multistate models, and developing statistical software.

Dr. Natalia Kunst is a Senior Research Fellow at the Centre for Health Economics at the University of York, with additional affiliations at the Yale University Schools of Public Health and Medicine and Harvard Medical School & Harvard Pilgrim Health Care Institute. Dr. Kunst is a decision sciences and health economics researcher with interests in uncertainty and evidence in decision-analytic modelling and health economic evaluations, value of information analysis, and health disparities.