Abstract
Value of Information (VoI) is a decision-theoretic approach to estimating the expected benefits from collecting further information of different kinds, in scientific problems based on combining one or more sources of data. VoI methods can assess the sensitivity of models to different sources of uncertainty, and help to set priorities for further data collection. They have been widely applied in healthcare policy making, but the ideas are general to a range of evidence synthesis and decision problems. This paper will give a broad overview of VoI methods, explaining the principles behind them, the range of problems that can be tackled with them, and how they can be implemented, and discuss the ongoing challenges in the area.
| Original language | English |
|---|---|
| Pages (from-to) | 95-118 |
| Number of pages | 24 |
| Journal | Annual Review of Statistics and Its Application |
| Volume | 9 |
| Early online date | 14 Oct 2021 |
| DOIs | |
| Publication status | Published - Mar 2022 |
Bibliographical note
Funding Information:Thanks to all the members of ConVOI (Collaborative Network for Value of Information) and to Howard Thom for helpful suggestions. C.H.J. was funded by the Medical Research Council, program number MRC_MC_UU_00002/11.
Publisher Copyright:
© 2022 Annual Reviews Inc.. All rights reserved.
Research Groups and Themes
- HEHP@Bristol
- HEB (Health Economics Bristol)
Keywords
- decision theory
- evidence synthesis
- health economics
- Bayesian
- sensitivity analysis
- design
Access to Document
- Full-text PDF (submitted manuscript)
This is a pre-print version of the article (version of record). It first appeared online via Annual Reviews at https://doi.org/10.1146/annurev-statistics-040120-010730. Please refer to any applicable terms of use of the publisher.