What is value of information analysis?

The application of value of information methods to a health economic decision, typically used to judge whether further research is worth conducting. Source: Claxton & Posnett 1996

What practical question does value of information analysis answer?

Value of information analysis is used to decide whether commissioning further research on a health decision is worthwhile. By estimating how much better decisions would become if current uncertainty were reduced, and setting that expected benefit against the cost of a study, it indicates whether the research would repay its cost and, if so, which study offers the best return. It thus turns the question of what to research into an explicit comparison of value and cost. Its answer guides research funding. Claxton and Sculpher (2006) describe this application. Source: Claxton & Sculpher 2006

How is value of information analysis conducted?

Value of information analysis is conducted using a probabilistic decision model that characterises parameter uncertainty: the expected value of perfect information is computed to gauge overall decision uncertainty, the expected value of partial perfect information to identify the most influential parameters, and the expected value of sample information to value specific studies, each scaled to the population over the decision relevance period. Combining sample information value with research costs gives the expected net benefit of sampling. These results indicate whether research is worthwhile and which studies are most efficient. Source: Raiffa & Schlaifer 1961

Why is value of information analysis used?

Value of information analysis is used to decide whether further research on a decision is worthwhile and to prioritise and design that research, by quantifying the expected benefit of reducing uncertainty against its cost. It shows whether decision uncertainty is large enough to justify research, which parameters most warrant investigation, and which study designs offer the best value. This provides a principled, decision-focused basis for allocating research resources, helping direct evidence collection to where it most improves decisions rather than relying on convention, so research investment yields greater benefit. Source: Claxton & Posnett 1996

What questions does value of information analysis answer?

Value of information analysis answers whether further research on a decision could be worthwhile, using population EVPI as an upper bound; which parameters or uncertainties are most worth investigating, using EVPPI; which specific studies are valuable and at what sample size and design, using the expected value of sample information and the expected net benefit of sampling; and how to prioritise research across decisions. Together these address whether, on what, and how to conduct research, linking the analysis of uncertainty to decisions about collecting further evidence. Source: Claxton & Posnett 1996

What are the limitations of value of information analysis?

Value of information analysis depends on the decision model and its assumed parameter distributions, and on the population size and decision relevance period, all of which involve uncertainty and forecasting, so its results are approximate, and the calculations, particularly for partial and sample information, can be computationally demanding. It captures value through the modelled decision, potentially missing broader scientific or equity considerations. These limitations mean value of information analysis informs rather than dictates research decisions, conducted with appropriate methods, transparent assumptions, and sensitivity analysis, alongside judgement about feasibility and wider goals. Source: Raiffa & Schlaifer 1961

Value of Information Analysis | HealthEconomics.wiki

Concept Architecture

Concept

Theoretically, Value of Information Analysis (VOI Analysis) is a quantitative decision-analytic framework that evaluates 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, value of information analysis is used to determine whether further research is worthwhile, prioritise research investments and optimise evidence generation.

Mathematically, value of information 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, value of information 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
Expected Value of Perfect Information
Expected Value of Partial Perfect Information
Expected Value of Sample Information
Expected Net Benefit of Sampling
Population EVPI
Bayesian Decision Theory
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 value of information 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.

Library

Publications

BookFeatured
Decision Modelling for Health Economic Evaluation — Briggs, Claxton & Sculpher, 1st Edition ed., 2006 (Oxford University Press)
Foundational textbook on decision-analytic modelling for economic evaluation, covering decision trees, Markov models, handling parameter and structural uncertainty, probabilistic sensitivity analysis, and value of information. Volume 1 in the Handbooks in Health Economic Evaluation series.
BookView source →
Book
Bayesian Methods in Health Economics — Gianluca Baio, 1st Edition ed., 2012 (Chapman & Hall / CRC Press)
An overview of Bayesian statistical methods for the analysis of health economic data, covering economic evaluation concepts, statistical cost-effectiveness analysis, Bayesian computation and MCMC, and applied health economic evaluation.
BookView source →
Book
Bayesian Cost-Effectiveness Analysis with the R package BCEA — Baio, Berardi & Heath, 1st Edition ed., 2017 (Springer)
A guide to health economic evaluation and cost-effectiveness modelling from a Bayesian statistical perspective, showing how to post-process model results, run probabilistic sensitivity analysis and value-of-information analysis using the BCEA R package and its web interface. Part of the Use R! series.
BookView source →
Journal article
Value of Information Analysis for Research Decisions — An Introduction: Report 1 of the ISPOR Value of Information Analysis Emerging Good Practices Task Force — Fenwick, Steuten, Knies, Ghabri, Basu, Murray, Koffijberg, Strong, Sanders Schmidler & Rothery, Vol. 23, No. 2 ed., 2020 (Value in Health)
The introductory ISPOR good-practice report on value-of-information (VOI) analysis, explaining how VOI quantifies the value of reducing decision uncertainty through further research and where it fits in resource-allocation decisions.
Journal ArticleView source →
Journal article
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 →

Media

Media
The Academic Health Economists’ Blog — The Academic Health Economists’ Blog contributors, Ongoing series ed., 2024 (The Academic Health Economists’ Blog)
A long-running community blog reviewing new health-economics journal papers, methods debates and books — a running commentary on the field’s published research aimed at academics and students.
Web (Blog)View source →
Media
BCEA: Bayesian Cost-Effectiveness Analysis with R (Tutorials) — Gianluca Baio, Andrea Berardi & Anna Heath, Package documentation ed., 2023 (BCEA project)
Tutorials for the BCEA R package, demonstrating Bayesian post-processing of probabilistic cost-effectiveness models — acceptability curves, EVPI/EVPPI and standardised value-of-information graphics.
Tutorial (Web)View source →

Tools & Resources

OtherFeatured
SAVI — Sheffield Accelerated Value of Information — Mark Strong, Jeremy Oakley & Penny Breeze (University of Sheffield), Web application ed., 2024 (University of Sheffield)
A free, open-access web calculator that computes value-of-information measures (EVPI, partial EVPI/EVPPI and EVSI) directly from a model’s probabilistic sensitivity analysis output — no need to re-run the model. Also reports payer strategy-specific and uncertainty burden.
Web Tool (R Shiny)View source →
Other
BCEAweb — Bayesian Cost-Effectiveness Analysis Web Interface — Gianluca Baio, Andrea Berardi & Anna Heath, Web application ed., 2023 (University College London)
A user-friendly web front-end to the BCEA R package: users upload probabilistic model output and obtain standardised cost-effectiveness summaries — cost-effectiveness planes, acceptability curves, EVPI and EVPPI — without writing R code.
Web Tool (R Shiny)View source →
OtherFeatured
heemod — Markov Models for Health Economic Evaluations (R package) — Antoine Filipovic-Pierucci, Kevin Zarca & Isabelle Durand-Zaleski, R package ed., 2023 (CRAN)
An R package for building Markov models for health economic evaluation, implementing the modelling and reporting features of standard reference textbooks: PSA, DSA, heterogeneity analysis, semi-Markov and non-homogeneous models, EVPI and budget-impact features.
Software (R package)View source →
Other
BCEA — Bayesian Cost-Effectiveness Analysis (R package) — Gianluca Baio, Andrea Berardi & Anna Heath, R package ed., 2023 (CRAN)
An R package for post-processing the output of a probabilistic cost-effectiveness model in a Bayesian framework — producing acceptability curves, EVPI/EVPPI, expected incremental benefit and standardised value-of-information graphics.
Software (R package)View source →
Other
dampack — Decision-Analytic Modeling Package (R package) — Fernando Alarid-Escudero, Greg Knowlton, Caleb Easterly & Eva Enns, R package ed., 2023 (CRAN)
An R package of tools for analysing and visualising the output of decision-analytic models — cost-effectiveness analysis, one- and two-way sensitivity analysis, probabilistic sensitivity analysis, and value-of-information analysis.
Software (R package)View source →
Other
TreeAge Pro — Decision Analysis & Modeling Software — TreeAge Software, LLC, Commercial software ed., 2024 (TreeAge Software)
A widely used commercial visual modelling platform for building decision trees, Markov and microsimulation models for cost-effectiveness analysis, with built-in sensitivity and value-of-information analysis — a long-standing industry standard in HTA modelling.
Software (Desktop, Commercial)View source →

Frequently Asked Questions (6)

What is value of information analysis?
The application of value of information methods to a health economic decision, typically used to judge whether further research is worth conducting.

Source: Claxton & Posnett 1996
What practical question does value of information analysis answer?
Value of information analysis is used to decide whether commissioning further research on a health decision is worthwhile. By estimating how much better decisions would become if current uncertainty were reduced, and setting that expected benefit against the cost of a study, it indicates whether the research would repay its cost and, if so, which study offers the best return. It thus turns the question of what to research into an explicit comparison of value and cost. Its answer guides research funding. Claxton and Sculpher (2006) describe this application.

Source: Claxton & Sculpher 2006
How is value of information analysis conducted?
Value of information analysis is conducted using a probabilistic decision model that characterises parameter uncertainty: the expected value of perfect information is computed to gauge overall decision uncertainty, the expected value of partial perfect information to identify the most influential parameters, and the expected value of sample information to value specific studies, each scaled to the population over the decision relevance period. Combining sample information value with research costs gives the expected net benefit of sampling. These results indicate whether research is worthwhile and which studies are most efficient.

Source: Raiffa & Schlaifer 1961
Why is value of information analysis used?
Value of information analysis is used to decide whether further research on a decision is worthwhile and to prioritise and design that research, by quantifying the expected benefit of reducing uncertainty against its cost. It shows whether decision uncertainty is large enough to justify research, which parameters most warrant investigation, and which study designs offer the best value. This provides a principled, decision-focused basis for allocating research resources, helping direct evidence collection to where it most improves decisions rather than relying on convention, so research investment yields greater benefit.

Source: Claxton & Posnett 1996
What questions does value of information analysis answer?
Value of information analysis answers whether further research on a decision could be worthwhile, using population EVPI as an upper bound; which parameters or uncertainties are most worth investigating, using EVPPI; which specific studies are valuable and at what sample size and design, using the expected value of sample information and the expected net benefit of sampling; and how to prioritise research across decisions. Together these address whether, on what, and how to conduct research, linking the analysis of uncertainty to decisions about collecting further evidence.

Source: Claxton & Posnett 1996
What are the limitations of value of information analysis?
Value of information analysis depends on the decision model and its assumed parameter distributions, and on the population size and decision relevance period, all of which involve uncertainty and forecasting, so its results are approximate, and the calculations, particularly for partial and sample information, can be computationally demanding. It captures value through the modelled decision, potentially missing broader scientific or equity considerations. These limitations mean value of information analysis informs rather than dictates research decisions, conducted with appropriate methods, transparent assumptions, and sensitivity analysis, alongside judgement about feasibility and wider goals.

Source: Raiffa & Schlaifer 1961

Trust Record

Verified by Dr Darrin Baines

British health economist

Professional identity: darrinbaines.org

Verification date: 25 Sep 2026

Content version: 1.0.0

Canonical Identity

Persistent URI: https://healtheconomics.wiki/concept/value-of-information-analysis
Term code: HE-EM-VI-037

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Value of Information Analysis