Evidence map›Paper›PMID 40518518›Full record

ArticleHealth and quality of life outcomes2025

Optimal DCE design for modelling nonlinear time preferences in EQ-5D-5L valuation studies: exploration of data from Denmark and Peru.

Alice Yu, Deborah Street, Marcel Jonker, Sterre Bour, Brendan Mulhern, Federico Augustovski, Cathrine Jensen, Claire Gudex, Morten Jensen, Romina Tejada and 3 more

Abstract read
In one paragraph

Article in Health and quality of life outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Valuation of the EQ-5D-Y-5L Using DCE Methods That Account for Nonlinear Time Preferences.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Alice YuCentre for Health Economics Research and Evaluation (CHERE), Faculty of Health, University of Technology Sydney (UTS), Sydney, NSW, Australia.
Deborah StreetCentre for Health Economics Research and Evaluation (CHERE), Faculty of Health, University of Technology Sydney (UTS), Sydney, NSW, Australia.
Marcel JonkerErasmus School of Health Policy & Management, Erasmus University, Rotterdam, Netherlands.
Sterre BourErasmus School of Health Policy & Management, Erasmus University, Rotterdam, Netherlands.
Brendan MulhernCentre for Health Economics Research and Evaluation (CHERE), Faculty of Health, University of Technology Sydney (UTS), Sydney, NSW, Australia. Brendan.Mulhern@uts.edu.au.
Federico AugustovskiHealth Technology Assessment and Health Economics Department of the Institute for Clinical Effectiveness and Health Policy (IECS-CONICET), University of Buenos Aires, Buenos Aires, Argentina.
Cathrine JensenDanish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Gistrup, Denmark.
Claire GudexDepartment of Clinical Research, University of Southern Denmark, Odense, Denmark.
Morten JensenDepartment of Economics and Business Economics, Business and Social Sciences, Aarhus University, Aarhus, Denmark.
Romina TejadaDivision of Cancer Epidemiology, Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada.
Richard NormanSchool of Population Health, Curtin University, Perth, Australia.
Rosalie VineyCentre for Health Economics Research and Evaluation (CHERE), Faculty of Health, University of Technology Sydney (UTS), Sydney, NSW, Australia.
Elly StolkErasmus School of Health Policy & Management, Erasmus University, Rotterdam, Netherlands.

Funding

University of Technology Sydney Chancellors Research Scholarship
6 · The paper itself

Abstract

backgroundDiscrete choice experiment (DCE) methods are an increasingly popular valuation method, particularly for the EQ-5D-5L. While EQ-5D-5L value sets developed using DCE have traditionally assumed linear time preferences, this assumption has been challenged. This has led to the development of DCE modelling methods that allow for nonlinear time preferences. The aim of this study was to explore the impact of a model that accounts for nonlinear time preferences with DCE choice set formats and design construction methods for EQ-5D-5L value sets.

methodsThis study used a four-arm (2 × 2) between-subjects design to investigate the impact of two commonly used DCE choice set formats (i.e. a third option of either immediate death or full health) and two commonly used DCE design construction methods (i.e. generator-developed and efficient designs) on EQ-5D-5L value sets. Mixed logit models that used exponential discounting to account for nonlinear time preferences were estimated in OpenBUGS. This was tested in a sample of respondents from Peru (n = 942) and Denmark (n = 988).

resultsAcross all arms and for both countries, discounting was found to be present when modelling explicitly for nonlinear time preferences. Although estimated discount rates varied widely from 1 to 117%, both type of choice set format and type of design construction method influenced the utilities for more severe health states. Choice sets with full health tended to produce a wider range of utility weights, while choice sets with immediate death tended to produce higher estimated discount rates. Generator-developed designs tended to produce the highest and lowest utility weights for health states compared to the efficient designs.

conclusionsThis study provides a comparison of DCE choice set format and design construction method when nonlinear time preferences were explicitly modelled. Limitations to this study are discussed including data quality issues with the Peruvian dataset and small sample sizes. Further investigation is needed to confirm the suitability of models that account for nonlinear time preferences in EQ-5D-5L valuation studies.

Indexed as

Choice BehaviorQuality of LifeAdultDenmarkFemaleHealth StatusHumansMaleMiddle AgedNonlinear DynamicsPeruResearch DesignSurveys and QuestionnairesTime FactorsDiscrete choice experimentEfficient designsEQ-5D-5LGenerator developed designsNonlinear modellingQuality of life

Identifiers

PMID40518518
PMCPMC12168295

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.