Improving Study Design and Reporting for Stated Choice Experiments [Methods Study], Australia, 2013-2020 (ICPSR 39714)
Version Date: Mar 12, 2026 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Alan R. Ellis, North Carolina State University
https://doi.org/10.3886/ICPSR39714.v1
Version V1
Summary View help for Summary
Researchers can use experiments to learn about what patients prefer. Discrete choice experiments, or DCEs, describe treatments with different features, such as out-of-pocket costs or wait times. Patients fill out surveys about which treatments they prefer. From their choices, researchers learn what is most important to patients and how they think about the different features.
DCEs can be hard to design and analyze. When surveys are complex, patients may ignore information or take shortcuts, which leads to inaccurate results.
To make DCE results more accurate, researchers can
- Change the design of the DCE
- Apply statistical methods
But current knowledge of how to do this is limited. In this project, the research team looked at improving methods to design and analyze DCEs.
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Study Purpose View help for Study Purpose
To improve understanding of the effects of selected DCE design features and statistical model assumptions on DCE results.
Study Design View help for Study Design
First, the research team examined how different DCE designs affect study estimates. DCEs have two parts: a pilot study and a main study. The team created simulated DCE pilot and main studies by replicating two empirical DCEs in a simulated population of 100,000 individuals. They generated 864 simulations representing variations in DCE design such as sample size and the prevalence, correlations, and interactions of different variables. Using different analytic models, the team assessed estimation errors due to DCE design.
Next, the research team examined the effects of using Halton draws on estimates from a random parameter logit model. Halton draws are a sampling technique that generates random data points simulating the overall population. The random parameter logit model assumes that parameters, such as the strength of preference for a certain healthcare feature, are random and vary across individuals. The team identified the number of Halton draws and the number of parameters for generating accurate results.
DCE researchers helped design the study.
Data Source View help for Data Source
"Simulated data for 100,000 participants based on results from two empirical data sets: Study 1 examined preferences for organ allocation among adults (N=2,051) in the Australian general public. Study 2 examined preferences for labor induction among women (N=362) who were participating in a randomized trial of labor induction alternatives in South Australia. "
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