Related Experiment Video
Updated: May 8, 2026
13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
Valuing SF-6D Health States Using a Discrete Choice Experiment
Richard Norman1, Rosalie Viney1, John Brazier2
1Centre for Health Economics Research and Evaluation, University of Technology, Sydney, Australia (RN, RV, PC)
Summary
Discrete choice experiments (DCEs) provide new Australian SF-6D utility weights, overcoming standard gamble limitations. These weights, reflecting physical functioning and mental health, are crucial for cost-utility analyses.
Area of Science:
- Health Economics
- Psychometrics
- Biostatistics
Background:
- Standard gamble (SG) methods for SF-6D utility weights show a floor effect.
- Discrete choice experiments (DCEs) offer an alternative elicitation technique for utility weights.
- Previous methods lacked Australian-specific SF-6D utility weights.
Purpose of the Study:
- To derive Australian utility weights for the SF-6D using DCE methods.
- To explore the application of DCEs for estimating utility weights.
- To provide an algorithm for Australian cost-utility analyses.
Main Methods:
- An online DCE was administered to 1017 Australian participants.
- Random-effects probit models estimated preferences, including nonlinearities in life expectancy.
- A preference index was calculated, valuing full health at 1 and death at 0.
Main Results:
- Physical functioning, pain, mental health, and vitality significantly influenced utility weights.
- Combining health state levels did not negatively impact model fit.
- The range of utility weights was wider than international SG-derived weights, with 5% of states valued below zero.
Conclusions:
- DCEs are effective for estimating utility weights for multi-attribute instruments like the SF-6D.
- Australian cost-utility analyses can now utilize these domestic SF-6D weights.
- Further research should compare DCE-derived weights with other elicitation methods for quality-adjusted life-year calculations.
Related Concept Videos
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...
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
01:15Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
01:15Bioavailability Study Design: Healthy Subjects Versus Patients
Bioavailability studies are essential for evaluating a drug's therapeutic efficacy and understanding its absorption patterns under various physiological conditions. Conducting such studies on target patient populations provides more relevant data by simulating real-world disease states. However, practical challenges often necessitate the use of young, healthy adult volunteers as study subjects.Patients may exhibit altered drug absorption patterns due to the effects of the disease itself,...
01:12What is an Experiment?
An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
01:16Experimental Designs
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...