What Can Discrete-Choice Experiments Tell Us about Patient Preferences? An Introduction to Quantitative Analysis of Choice Data

F. Reed Johnson (), Wiktor Adamowicz and Catharina Groothuis-Oudshoorn
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F. Reed Johnson: Duke University
Wiktor Adamowicz: University of Alberta
Catharina Groothuis-Oudshoorn: University of Twente

The Patient: Patient-Centered Outcomes Research, 2025, vol. 18, issue 5, No 3, 425-440

Abstract: Abstract This paper provides an introduction to statistical analysis of choice data using example data from a simple discrete-choice experiment (DCE). It describes the layout of the analysis dataset, types of variables contained in the dataset, and how to identify response patterns in the data indicating data quality. Model-specification options include linear models with continuous attribute levels and non-linear continuous and categorical attribute levels. Advantages and disadvantages of conditional logit, mixed logit, and latent-class analysis are discussed and illustrated using the example DCE data. Readers are provided with links to various software programs for analyzing choice data. References are provided on topics for which there currently is limited consensus and on more advanced techniques to guide readers interested in exploring choice-modeling challenges in greater depth. Supplementary materials include the simulated example data used to illustrate modeling approaches, together with R and Matlab code to reproduce the estimates shown.

Date: 2025
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DOI: 10.1007/s40271-024-00705-7

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