TY - JOUR
T1 - The Fold-in, Fold-out Design for DCE Choice Tasks
T2 - Application to Burden of Disease
AU - Goossens, Lucas M. A.
AU - Jonker, Marcel F.
AU - Rutten-van Molken, Maureen P. M. H.
AU - Boland, M. R. S.
AU - Slok, Annerika H. M.
AU - Salome, P. L.
AU - van Schayck, Onno C. P.
AU - 't Veen, Johannes C. C. M. In
AU - Stolk, E. A.
AU - Donkers, Bas
AU - research team that developed the ABC tool
N1 - Funding Information:
Erasmus School of Health Policy and Management & Institute for Medical Technology Assessment, Erasmus University Rotterdam, Rotterdam, the Netherlands (LMAG, MFJ, MPMHRvM, MRSB, EAS); Erasmus Choice Modelling Centre, Erasmus University Rotterdam, Rotterdam, the Netherlands (LMAG, MFJ, MPMHRvM, EAS, BD); Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands (BD); CAPHRI Care and Public Health Research Institute, Maastricht University, Maastricht, the Netherlands (AHMS, OCPvS); UNICUM Huisartsenzorg, Bilthoven, the Netherlands (PLS); Department of Pulmonology, Franciscus Gasthuis en Vlietland, Rotterdam, the Netherlands (JCCMiV); and EuroQol Foundation, Rotterdam, the Netherlands (EAS). The members of the ABC tool research team are listed in the Acknowledgement section. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors received funding for this study from the Innovation Fund Dutch Health Insurers, Zeist, the Netherlands; Picasso for COPD, Alkmaar, the Netherlands; GlaxoSmithKline BV, Zeist, the Netherlands; AstraZeneca BV, Zoetermeer, the Netherlands; Novartis BV, Arnhem, the Netherlands; Chiesi Pharmaceuticals BV, Rijswijk, the Netherlands; and Almirall BV, Utrecht, the Netherlands.
Funding Information:
The authors thank 2 anonymous reviewers for their constructive and useful comments. Members of the of the research team that developed the ABC tool are as follows: G.M. Asijee (Maastricht University, CAPHRI School for Public Health and Primary care, Department of Family Medicine, Maastricht, The Netherlands & Foundation PICASSO for COPD, Alkmaar, The Netherlands), M.R.S. Boland (Erasmus University, Erasmus School for Health Policy and Management & institute for Medical Technology Assessment, Rotterdam, The Netherlands), G. van Breukelen (Maastricht University, CAPHRI School for Public Health and Primary Care, Department of Methodology & Statistics, Maastricht, The Netherlands), N.H. Chavannes (Leiden University Medical Centre, Department of Public Health and Primary Care, Leiden, The Netherlands), P.N. Dekhuijzen (Radboud University Medical Center, Department of Pulmonary Diseases, Nijmegen, The Netherlands), B. Donkers (Erasmus University, Erasmus School of Economics, Department of Business Economics, Rotterdam, The Netherlands), L.M.A. Goossens (Erasmus University, Erasmus School for Health Policy and Management & institute for Medical Technology Assessment, Rotterdam, The Netherlands), M.F. Jonker (Erasmus University, Erasmus School for Health Policy and Management & institute for Medical Technology Assessment, Rotterdam, The Netherlands), S. Holverda (Lung Foundation Netherlands, Amersfoort, The Netherlands), H.A.M. Kerstjens (University Medical Centre Groningen, Department of Pulmonary Diseases & Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands), D. Kotz (Maastricht University, CAPHRI School for Public Health and Primary care, Department of Family Medicine, Maastricht, The Netherlands & Institute of General Practice, Medical Faculty of the Heinrich-Heine-University Düsseldorf, Germany), T. van der Molen (University Medical Centre Groningen, Department of General Practice & Groningen Research Institute for Asthma and COPD (GRIAC), Groningen, The Netherlands), MPMH Rutten-van Mölken (Erasmus University, Erasmus School for Health Policy and Management & institute for Medical Technology Assessment, Rotterdam, The Netherlands), P.L. Salomé (Huisartsencoöperatie PreventZorg, Bilthoven, The Netherlands), C.P. van Schayck (Maastricht University, CAPHRI School for Public Health and Primary care, Department of Family Medicine, The Netherlands), A.H.M. Slok (Maastricht University, CAPHRI School for Public Health and Primary care, Department of Family Medicine, Maastricht, The Netherlands), E.A. Stolk (Euroqol Foundation, Rotterdam, The Netherlands, and Erasmus University, Erasmus School for Health Policy and Management & institute for Medical Technology Assessment, Rotterdam, The Netherlands), M. Twellaar (Maastricht University, CAPHRI School for Public Health and Primary care, Department of Family Medicine, The Netherlands), J.C.C.M. in’t Veen (Sint Franciscus Gasthuis, Department of Pulmonology, Rotterdam, The Netherlands).
Publisher Copyright:
© The Author(s) 2019.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - Discrete choice experiments
KW - Task complexity
KW - Preference measurement
KW - COPD
KW - Burden of disease
KW - HIERARCHICAL INFORMATION INTEGRATION
KW - HEALTH
KW - PREFERENCES
KW - COMPLEXITY
KW - MODEL
U2 - 10.1177/0272989X19849461
DO - 10.1177/0272989X19849461
M3 - Article
C2 - 31142198
SN - 0272-989X
VL - 39
SP - 450
EP - 460
JO - Medical Decision Making
JF - Medical Decision Making
IS - 4
ER -