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Med Decis Making. 2019 May;39(4):450-460. doi: 10.1177/0272989X19849461. Epub 2019 May 29.

The Fold-in, Fold-out Design for DCE Choice Tasks: Application to Burden of Disease.

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Erasmus School of Health Policy and Management & Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, the Netherlands.
Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, the Netherlands.
CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands.
UNICUM Huisartsenzorg, Bilthoven, the Netherlands.
Department of Pulmonology, Franciscus Gasthuis en Vlietland, Rotterdam, the Netherlands.
EuroQol Foundation, Rotterdam, the Netherlands.
Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands.


Background In discrete-choice experiments (DCEs), choice alternatives are described by attributes. The importance of each attribute can be quantified by analyzing respondents' choices. Estimates are valid only if alternatives are defined comprehensively, but choice tasks can become too difficult for respondents if too many attributes are included. Several solutions for this dilemma have been proposed, but these have practical or theoretical drawbacks and cannot be applied in all settings. The objective of the current article is to demonstrate an alternative solution, the fold-in, fold-out approach (FiFo). We use a motivating example, the ABC Index for burden of disease in chronic obstructive pulmonary disease (COPD). Methods Under FiFo, all attributes are part of all choice sets, but they are grouped into domains. These are either folded in (all attributes have the same level) or folded out (levels may differ). FiFo was applied to the valuation of the ABC Index, which included 15 attributes. The data were analyzed in Bayesian mixed logit regression, with additional parameters to account for increased complexity in folded-out questionnaires and potential differences in weight due to the folding status of domains. As a comparison, a model without the additional parameters was estimated. Results Folding out domains led to increased choice complexity for respondents. It also gave domains more weight than when it was folded in. The more complex regression model had a better fit to the data than the simpler model. Not accounting for choice complexity in the models resulted in a substantially different ABC Index. Conclusion Using a combination of folded-in and folded-out attributes is a feasible approach for conducting DCEs with many attributes.


Burden of disease; COPD; Discrete choice experiments; Preference measurement; Task complexity

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