Concept Architecture
Concept
Theoretically, Expected Net Benefit of Sampling (ENBS) Calculation is a value of information method used to determine whether conducting additional research is economically worthwhile after accounting for the costs of that research. It is founded on Bayesian decision theory and extends the Expected Value of Sample Information (EVSI) framework by explicitly incorporating research costs. In health economics, ENBS calculations support optimal research prioritisation by identifying whether proposed studies generate sufficient expected value to justify their implementation.
Mathematically, ENBS is calculated as the difference between the expected value of sample information and the total expected cost of obtaining that information. A positive ENBS indicates that the expected benefit of reducing decision uncertainty exceeds the research cost, whereas a negative ENBS indicates that conducting the study is not economically justified.
In practice, ENBS calculations are performed after estimating EVSI and determining the total costs of conducting the proposed study, including recruitment, follow-up, data collection and analysis. ENBS is routinely used in health technology assessment and research prioritisation to compare alternative study designs, optimise sample sizes and determine whether further evidence collection represents good value for money.
Purpose
Used to determine whether additional research is economically worthwhile by comparing the expected value of reducing decision uncertainty with the expected cost of conducting the research.
Mathematical Formulae
Primary Formula
ENBS = EVSI ? C
where:
ENBS = Expected Net Benefit of Sampling
EVSI = Expected Value of Sample Information
C = total expected research cost
Supporting Formulae
Decision rule:
If ENBS > 0, additional research is economically justified.
If ENBS � 0, additional research is not economically justified.
Population ENBS:
Population ENBS = Population EVSI ? Population Research Cost
Related Mathematical Methods
- Expected Value of Sample Information
- Expected Value of Perfect Information
- Expected Value of Partial Perfect Information
- Bayesian Decision Theory
- Value of Information Analysis
- Research Prioritisation
Example
A proposed clinical trial has an estimated Expected Value of Sample Information of �7.2 million. The total projected cost of conducting the trial is �4.9 million.
ENBS = �7.2 million ? �4.9 million
ENBS = �2.3 million
Because the ENBS is positive, the expected value of reducing decision uncertainty exceeds the cost of conducting the research, indicating that the study is economically worthwhile.
Excel Implementation
| Function | Example Formula | Health Economics Application |
|---|---|---|
| SUM | =SUM(B2:B20) | Calculate the total expected research cost. |
| AVERAGE | =AVERAGE(ResultRange) | Estimate the expected value of sample information from simulation outputs. |
| IF | =IF(B2-C2>0,"Research justified","Research not justified") | Determine whether ENBS is positive. |
| MAX | =MAX(ENBSRange) | Compare alternative study designs by selecting the highest ENBS. |
VBA (Optional)
VBA can automate ENBS calculations across multiple study designs, sample sizes and research costs to identify the economically optimal research strategy.
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.
- Fenwick E, Claxton K, Sculpher M. Representing uncertainty: the role of cost-effectiveness acceptability curves. Health Economics. 2001;10(8):779?787.
- NICE. NICE Health Technology Evaluations: The Manual.
- ISPOR Value of Information Good Practice Reports.
Related Concepts (2)
Library
Tools & Resources
1
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 →
Frequently Asked Questions (6)
What is ENBS calculation?
The process of computing the expected net benefit of sampling, the expected value of sample information from a study minus its expected cost.
Source: Claxton & Posnett 1996
What is subtracted from what in an ENBS calculation?
An expected net benefit of sampling calculation takes the expected value of the sample information a proposed study would provide and subtracts the expected cost of running that study. The first term is the health-and-cost benefit expected from the better decisions the study's results would allow, converted to a common scale, while the second is the price of the research. A positive difference means the study is worth doing, and the size of the difference ranks competing studies. It is a benefit-minus-cost comparison for research. Claxton and Sculpher (2006) set out this calculation.
Source: Claxton & Sculpher 2006
How is the expected net benefit of sampling calculated?
The expected net benefit of sampling is calculated by taking the expected value of sample information for a proposed study, which is the expected gain from the better decisions its data would enable, and subtracting the expected cost of conducting the study. The expected value of sample information is obtained from value-of-information analysis for the given study design and sample size, and the cost includes the research and any associated costs. The difference is the expected net benefit of sampling, indicating the net worth of the study.
Source: Raiffa & Schlaifer 1961
Why is the expected net benefit of sampling computed?
The expected net benefit of sampling is computed to decide whether a proposed study is worthwhile and, by comparing it across designs, to identify the most efficient research, since it weighs the value of the information a study would provide against its cost. A positive value indicates the study is expected to be worth doing, and the design maximising it is the most efficient. This makes the expected net benefit of sampling a tool for prioritising and designing research so that resources are directed to studies whose value exceeds their cost.
Source: Claxton & Posnett 1996
How does ENBS relate to optimal sample size?
The expected net benefit of sampling relates to optimal sample size because it can be computed for different sample sizes: as the sample size grows, the expected value of sample information rises but so does the cost, so the expected net benefit of sampling typically increases, peaks, and then falls. The sample size that maximises the expected net benefit of sampling is the optimal one, balancing the value of additional information against its cost. So computing it across sample sizes identifies the most efficient study size.
Source: Raiffa & Schlaifer 1961
What are the limitations of ENBS calculation?
ENBS calculation depends on the value-of-information estimates, which rest on the model, its assumed parameter distributions, and the decision relevance period and population, and on the assumed study costs and design, so errors or uncertainty in these carry into the result. Computing the expected value of sample information can be demanding, and forecasting costs and the future decision context is uncertain. These limitations mean the expected net benefit of sampling is treated as an informative but approximate guide, with its sensitivity to the key assumptions examined.
Source: Claxton & Posnett 1996
Trust Record
Verified by Dr Darrin Baines
British health economist
Professional identity: darrinbaines.org
Verification date: 31 Oct 2025
Content version: 1.0.0
Canonical Identity
- Persistent URI
- https://healtheconomics.wiki/concept/enbs-calculation
- Term code
- HE-EM-VI-026
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