Projects per year
Abstract
Background: Value of information (VoI) calculations give the expected benefits of decision making under perfect information (EVPI) or sample information (EVSI), typically on the premise that any treatment recommendations made in light of this information will be implemented instantly and fully. This assumption is unlikely to hold in health care; evidence shows that obtaining further information typically leads to ‘improved’, rather than ‘perfect’ implementation.
Objectives: To present a method of calculating the expected value of further research which accounts for the reality of improved implementation.
Methods: This work extends an existing conceptual framework by introducing additional states of the world regarding information (sample information, in addition to current and perfect information) and implementation (improved implementation, in addition to current and optimal implementation). The extension allows calculating the ‘implementation-adjusted’ EVSI (IA-EVSI), a measure that accounts for different degrees of implementation. Calculations of implementation-adjusted estimates are illustrated under different scenarios through a stylised case study in non-small cell lung cancer.
Results: In the particular case study, the population values for EVSI and IA-EVSI were £25 million and £8 million, respectively, thus a decision assuming perfect implementation would have overestimated the expected value of research by about £17 million. IA-EVSI was driven by the assumed time horizon and, importantly, the specified rate of change in implementation: the higher the rate, the greater the IA-EVSI and the lower the difference between IA-EVSI and EVSI.
Conclusions: Traditionally calculated measures of population VoI rely on unrealistic assumptions about implementation. This article provides a simple framework which accounts for improved, rather than perfect, implementation, and offers more realistic estimates of the expected value of research.
Objectives: To present a method of calculating the expected value of further research which accounts for the reality of improved implementation.
Methods: This work extends an existing conceptual framework by introducing additional states of the world regarding information (sample information, in addition to current and perfect information) and implementation (improved implementation, in addition to current and optimal implementation). The extension allows calculating the ‘implementation-adjusted’ EVSI (IA-EVSI), a measure that accounts for different degrees of implementation. Calculations of implementation-adjusted estimates are illustrated under different scenarios through a stylised case study in non-small cell lung cancer.
Results: In the particular case study, the population values for EVSI and IA-EVSI were £25 million and £8 million, respectively, thus a decision assuming perfect implementation would have overestimated the expected value of research by about £17 million. IA-EVSI was driven by the assumed time horizon and, importantly, the specified rate of change in implementation: the higher the rate, the greater the IA-EVSI and the lower the difference between IA-EVSI and EVSI.
Conclusions: Traditionally calculated measures of population VoI rely on unrealistic assumptions about implementation. This article provides a simple framework which accounts for improved, rather than perfect, implementation, and offers more realistic estimates of the expected value of research.
Original language | English |
---|---|
Number of pages | 31 |
Journal | Medical Decision Making |
Early online date | 13 Nov 2015 |
DOIs | |
Publication status | E-pub ahead of print - 13 Nov 2015 |
Keywords
- Health economics
- economic assessment
- Economic evaluation, modelling
- IMPLEMENTATION
- priority setting for spending
- Priority setting
- Value of information
- Clinical trials
Fingerprint
Dive into the research topics of 'Adjusting estimates of the expected value of information for implementation: theoretical framework and practical application'. Together they form a unique fingerprint.Projects
- 1 Finished
-
Allocating Cancer Research Resources to Maximise Health Benefits
Andronis, L.
NIHR TRAINEES COORDINATING CENTRE
1/09/09 → 31/08/12
Project: Research