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Value of Information Analysis in Models to Inform Health Policy

Jackson, CH; Baio, G; Heath, A; Strong, M; Welton, NJ; Wilson, ECF; (2022) Value of Information Analysis in Models to Inform Health Policy. Annual Review of Statistics and Its Application , 9 (2022) 10.1146/annurev-statistics-040120-010730. (In press).

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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 article gives 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 discusses the ongoing challenges in the area. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Type: Article
Title: Value of Information Analysis in Models to Inform Health Policy
Location: United States
DOI: 10.1146/annurev-statistics-040120-010730
Publisher version: https://doi.org/10.1146/annurev-statistics-040120-...
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10136834
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