Evidence map›Paper›PMID 40660259›Full record

ArticleHealth and quality of life outcomes2025

An Australian Value Set for the EQ-5D-Y-3L.

Tianxin Pan, Bram Roudijk, Nancy Devlin, Brendan Mulhern, Richard Norman

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 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Do EQ-5D-Y-3L value sets have common properties, and how do they compare to EQ-5D-5L value sets?The European journal of health economics : HEPAC : health economics in prevention and care · 2026
    Article
  5. 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
  6. Article
  7. 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

5 authors.

Tianxin PanChid Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
Bram RoudijkEuroQol Research Foundation, Rotterdam, The Netherlands.
Nancy DevlinChid Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia.
Brendan MulhernCentre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, NSW, Australia.
Richard NormanCurtin School of Population Health, Faculty of Health Sciences, Curtin University, Perth, WA, Australia. Richard.Norman@curtin.edu.au.

Funding

EuroQol Research Foundation EQ Project 83-2020VS
6 · The paper itself

Abstract

backgroundAustralia has a well-established health technology assessment process and there is extensive use of generic health related quality of life (HRQoL) instruments in evidence presented to it. However, there are gaps in tools and evidence available to support evaluation of paediatric health. The aim of this paper is to produce an Australian EQ-5D-Y-3L (Y-3L) value set.

methodsThe methods follow the international Y-3L valuation protocol, but with an expanded design. Data were collected using Composite Time Trade Off (cTTO) and Discrete Choice Experiment (DCE) data from two independent samples of adult members of the Australian general public. In total, 52 Y-3L health states, assigned into four blocks of 14 health states each containing health state 33333, were valued using cTTO. cTTO data were collected via videoconferencing interview and each respondent valued 14 health states. Mean observed cTTO values were adjusted for censoring at -1 using a Tobit model. For the DCE component, 150 latent scale DCE choice pairs were collected via an online survey with each participant completing 15 pairs. DCE data were modelled using a garbage class mixed logit model. Two approaches to anchor DCE data to the Quality Adjusted Life Years (QALYs) scale were explored: anchoring on the value for the worst health state (33333); and mapping DCE data onto the mean cTTO values using all 52 health states. Two evaluation criteria were used to select the final value set: (1) coefficient significance and logical consistency; (2) prediction accuracy of the mean observed cTTO values.

resultsIn total, 268 individuals participated in the cTTO interviews, and 1002 completed the DCE. The linear mapping without intercept performed best and was selected as the final value set. Health state values ranged between 0.142 and 1. The relative importance of domains by level 3 coefficients (ordered from most to least important) was: pain/discomfort, then feeling worried, sad or unhappy, usual activities, looking after myself, and mobility.

conclusionThis study reports an Australian value set for the Y-3L, which enables the calculation of QALYs for use in the economic evaluation of paediatric interventions and can support evidence development and decision making.

Indexed as

Health StatusQuality of LifeAdolescentAdultAgedAustraliaFemaleHumansMaleMiddle AgedPsychometricsQuality-Adjusted Life YearsSurveys and QuestionnairesTechnology Assessment, BiomedicalYoung AdultDiscrete choice experimentEQ-5D-YEQ-5D-Y-3LPaediatrics, preferences, time Trade-OffValue set, utilities, Australia

Identifiers

PMID40660259
PMCPMC12261590

What Socratic 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 Socratic graph.