Related Experiment Video
Updated: Jun 18, 2026
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
Is there a case for using visual analogue scale valuations in cost-utility analysis?
1City Health Economics Centre, City University, London, UK. d.parkin@city.ac.uk
Health Economics
|February 25, 2006
Summary
The visual analogue scale (VAS) for health state valuation is theoretically sound and empirically advantageous for cost-utility analysis (CUA), despite common criticisms. Further research can solidify its role in health economics.
Area of Science:
- Health Economics
- Decision Analysis
- Psychometrics
Background:
- Visual analogue scale (VAS) valuations are frequently criticized in economic evaluation literature.
- Common criticisms include lack of theoretical foundation, non-choice-based nature, and inconsistency with utility theory.
- These criticisms question the validity of VAS for health state valuation in cost-utility analysis (CUA).
Purpose of the Study:
- To critically review theoretical and empirical arguments against VAS valuations.
- To address common criticisms of VAS in the context of CUA.
- To evaluate the suitability of VAS compared to alternative valuation methods.
Main Methods:
- Theoretical review of VAS properties and criticisms.
- Empirical assessment of VAS performance against alternative methods.
- Analysis of VAS consistency with non-welfarist foundations of QALYs and CUA.
Main Results:
- VAS valuations possess a theoretical basis consistent with CUA.
- Criticisms regarding choice, uncertainty, and context effects are addressed and often refuted.
- VAS demonstrates empirical advantages over alternative valuation methods in certain aspects.
Conclusions:
- There are strong grounds to dispute the consensus view against VAS valuations.
- VAS has significant advantages for health state valuation in CUA.
- Further research is needed to fully establish and consolidate the potential of VAS.
Related Concept Videos
00:55Ordinal Level of Measurement
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks in the...
00:54Ratio Level of Measurement
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...
01:09Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
01:05Estimation of the Physical Quantities
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
01:16Bioequivalence Data: Statistical Interpretation
The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
01:21Friedman Two-way Analysis of Variance by Ranks
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...