Concept Architecture
Concept
Theoretically, VOI Analysis is the abbreviated term for Value of Information Analysis, a decision-analytic framework that quantifies the expected benefit of reducing uncertainty before making healthcare decisions. It is founded on Bayesian decision theory and compares the expected consequences of decisions made with current information against those made with additional information. In health economics, VOI analysis is used to determine whether further research is worthwhile, prioritise research investments and optimise evidence generation.
Mathematically, VOI analysis estimates the expected increase in net benefit resulting from obtaining additional information. It encompasses a family of related measures, including Expected Value of Perfect Information, Expected Value of Partial Perfect Information, Expected Value of Sample Information and Expected Net Benefit of Sampling. These measures quantify different forms of uncertainty reduction and provide an economic basis for research prioritisation.
In practice, VOI analysis is performed using probabilistic sensitivity analysis combined with Bayesian decision models and simulation techniques. Health economists apply VOI analysis to estimate the societal value of future research, identify influential uncertain parameters, optimise study design and determine whether the expected benefits of additional evidence exceed the associated research costs.
Purpose
Used to quantify the economic value of reducing uncertainty, prioritise future research, optimise study design and support efficient healthcare decision-making.
Mathematical Formulae
Primary Formula
VOI = Expected Value with Additional Information ? Expected Value with Current Information
Supporting Formulae
Expected Value of Perfect Information:
EVPI = E[max(NMB)] ? max(E[NMB])
Expected Value of Partial Perfect Information:
EVPPI = E?[max(E�|?(NMB))] ? max(E[NMB])
Expected Value of Sample Information:
EVSI = E[max(E(NMB|Data))] ? max(E[NMB])
Expected Net Benefit of Sampling:
ENBS = EVSI ? C
where:
NMB = Net Monetary Benefit
C = expected research cost
Related Mathematical Methods
- Value of Information
- Value of Information Analysis
- Expected Value of Perfect Information
- Expected Value of Partial Perfect Information
- Expected Value of Sample Information
- Expected Net Benefit of Sampling
- Population EVPI
- Probabilistic Sensitivity Analysis
Example
A probabilistic sensitivity analysis identifies substantial uncertainty regarding the cost-effectiveness of a new medical technology. VOI analysis estimates an EVPI of �450 per patient, identifies treatment effectiveness as the principal contributor to uncertainty through EVPPI and calculates the EVSI and ENBS for a proposed clinical trial. The positive ENBS indicates that conducting further research is economically justified and supports investment in the proposed study.
Excel Implementation
| Function | Example Formula | Health Economics Application |
|---|---|---|
| MAX | =MAX(B2:D2) | Identify the intervention with the greatest net monetary benefit in each simulation. |
| AVERAGE | =AVERAGE(ResultRange) | Estimate expected net monetary benefit across simulations. |
| SUMPRODUCT | =SUMPRODUCT(EVPI_per_person,Population) | Calculate population value of information measures. |
| IF | =IF(ENBS>0,"Research justified","Research not justified") | Determine whether additional research is economically worthwhile. |
VBA (Optional)
VBA can automate probabilistic sensitivity analyses and calculate EVPI, EVPPI, EVSI and ENBS to support comprehensive VOI analyses.
Sources
- Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. Journal of Health Economics. 1999;18(3):341?364.
- Briggs A, Claxton K, Sculpher M. Decision Modelling for Health Economic Evaluation. Oxford University Press.
- Ades AE, Lu G, Claxton K. Expected value of sample information calculations in medical decision modelling. Medical Decision Making. 2004;24(2):207?227.
- ISPOR Value of Information Good Practice Reports.
- NICE. NICE Health Technology Evaluations: The Manual.
Related Concepts (2)
Library
Publications
1
Value of Information Analytical Methods: Report 2 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force — Rothery, Strong, Koffijberg, Basu, Ghabri, Knies, Murray, Sanders Schmidler, Steuten & Fenwick, Vol. 23, No. 3 ed., 2020 (Value in Health)
The methods companion to the ISPOR VOI series, giving detailed algorithms and software guidance for computing EVPI, EVPPI, EVSI and the expected net benefit of sampling, with recommendations for selecting methods by decision-problem features.
Journal ArticleView source →
Frequently Asked Questions (6)
What is VOI analysis?
An abbreviation for value of information analysis, applying formal decision-theoretic methods to quantify the expected benefit of reducing decision uncertainty.
Source: Claxton & Posnett 1996
What measures make up a value-of-information analysis?
A value-of-information analysis draws on a family of related measures that examine uncertainty at different depths. The expected value of perfect information gives the ceiling on what any research could be worth, the partial version identifies which particular parameters are worth resolving, and the sample-information measure values a specific realistic study. Used together, they move from whether research is worthwhile at all, through what to study, to how to design it. The abbreviation stands for this decision-theoretic toolkit. Claxton and Sculpher (2006) set out these measures.
Source: Claxton & Sculpher 2006
How does VOI analysis quantify the value of research?
VOI analysis quantifies the value of research by comparing expected decision outcomes with and without additional information: it computes the expected value of perfect information as an upper bound on research value, the expected value of partial perfect information to find which parameters matter most, and the expected value of sample information to value specific studies, all scaled to the population over the decision relevance period. Subtracting research costs gives the expected net benefit of sampling. These measures express the worth of research in the decision's own terms.
Source: Raiffa & Schlaifer 1961
Why is VOI analysis used in research decisions?
VOI analysis is used in research decisions because it links the uncertainty in an evaluation to the decisions it informs, showing whether reducing uncertainty through research would improve decisions enough to justify its cost. It indicates whether decision uncertainty warrants research at all, which parameters to investigate, and which study designs are most efficient. This provides a principled basis for prioritising and designing research, directing limited resources to studies that most improve decisions, rather than relying on convention or intuition, so evidence collection yields greater value.
Source: Claxton & Posnett 1996
What measures does VOI analysis use?
VOI analysis uses the expected value of perfect information, the value of resolving all uncertainty and an upper bound on research value; the expected value of partial perfect information, the value of resolving specific parameters, identifying which matter most; and the expected value of sample information, the value of a specific finite study. Scaled to the affected population over the decision relevance period and combined with research costs as the expected net benefit of sampling, these measures indicate whether, on what, and how to conduct research.
Source: Raiffa & Schlaifer 1961
What are the limitations of VOI analysis?
VOI analysis depends on the decision model and its assumed parameter distributions, and on the population size and decision relevance period, which require forecasting the future, so its estimates are approximate, and the calculations, especially for partial and sample information, can be computationally demanding. It captures value only through the modelled decision, potentially missing wider considerations. These limitations mean VOI analysis informs rather than dictates research decisions, conducted with appropriate methods, transparent assumptions, and sensitivity analysis, and interpreted alongside judgement about feasibility and broader research goals.
Source: Raiffa & Schlaifer 1961
Trust Record
Verified by Dr Darrin Baines
British health economist
Professional identity: darrinbaines.org
Verification date: 3 Nov 2025
Content version: 1.0.0
Canonical Identity
- Persistent URI
- https://healtheconomics.wiki/concept/voi-analysis
- Term code
- HE-EM-VI-028
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