Evidence map›Paper›PMID 39791109›Full record

ArticleEClinicalMedicine2025

Prediction accuracy of discrete choice experiments in health-related research: a systematic review and meta-analysis.

Ying Zhang, Thi Quynh Anh Ho, Fern Terris-Prestholt, Matthew Quaife, Esther de Bekker-Grob, Peter Vickerman, Jason J Ong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–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

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
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  4. Predicted Preferences for Tuberculosis Point-of-Care Tests Among Tuberculosis-Affected Individuals in 5 High Burden Countries.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026
    Article
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  6. Beyond the Average: Modeling Individual-Specific Preferences for Ulcerative Colitis Surgery.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2026
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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

7 authors.

Ying ZhangSchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Thi Quynh Anh HoDeakin Health Economics, Institute for Health Transformation, School of Health and Social Development, Faculty of Health, Deakin University, Melbourne, Victoria, Australia.
Fern Terris-PrestholtWarwick Medical School, University of Warwick, Coventry, United Kingdom.
Matthew QuaifePatient Centered Research, Evidera, London, United Kingdom.
Esther de Bekker-GrobErasmus School of Health Policy & Management, Erasmus University, Rotterdam, the Netherlands.
Peter VickermanBristol Medical School, University of Bristol, Bristol, United Kingdom.
Jason J OngSchool of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data. Methods: We searched six databases for health-related studies that used DCE to assess external validity and reported on predicted versus real-world choices, up to July 2024. A generalised linear mixed model was used for a meta-analysis to jointly pool the sensitivity and specificity. Heterogeneity was assessed using the Findings: We identified 14 relevant studies, of which 10 were included in the meta-analysis. Most studies were conducted in high-income countries (11/14, 79%) from the European region (9/14, 64%) and analysed using mixed logit models (5/14, 36%). Pooled sensitivity and specificity estimates were 89% (95% CI:77-95, Interpretation: DCEs are valuable for capturing health-related preferences and possess reasonable external validity to predict health-related behaviours, particularly for opt-in choices. Contextual factors (e.g., type of intervention, study setting, analysis method) influenced the predictive accuracy. Funding: JJO is supported by an Australian National Health and Medical Research Council Emerging Leadership Investigator Grant (GNT1193955). EBG is supported by the Dutch Research Council (NWO-Talent-Scheme-Vidi-Grant No, 09150171910002). YZ is supported by an Australian Government Research Training Program (RTP) scholarship.

Indexed as

Discrete choice experimentExternal validityPredictive accuracyRevealed preferencesStated preferences

Identifiers

PMID39791109
PMCPMC11714376

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.