Evidence map›Paper›PMID 40287928›Full record

ArticlePharmacoEconomics2025

Validation of the Cancer-Specific Preference-Based Measure EORTC QLU-C10D against the Generic Instruments EQ-5D-5L and SF-6Dv2 in a Prospectively Collected Sample of Patients with Cancer in Austria and France.

Micha J Pilz, Simone Seyringer, Virginie Nerich, Madeleine T King, Richard Norman, Eva M Gamper, EORTC Quality of Life Group

Abstract readValidation Study
In one paragraph

Article in PharmacoEconomics, 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. Health related quality of life of patients with breast cancer in Malaysia: evidence from EQ-5D-5L and EORTC QLQ-C30.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
    Article
  2. United Kingdom value set for the functional assessment of cancer therapy eight dimension (FACT-8D) preference-based quality of life instrument.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
    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

7 authors.

Micha J PilzHealth Outcomes Research Unit, University Hospital of Psychiatry II, Medial University of Innsbruck, 6020, Innsbruck, Austria.
Simone SeyringerDepartment of Nuclear Medicine, Medical University of Innsbruck, 6020, Innsbruck, Austria.ORCID http://orcid.org/0000-0003-0689-9160
Virginie NerichUniversité de Franche-Comté, CHU Besançon, INSERM, EFS-BFC, UMR 1098, Pôle Pharmacie, 25030, Besançon, France.
Madeleine T KingSchool of Psychology, University of Sydney, Sydney, NSW, Australia.
Richard NormanSchool of Population Health, Curtin University, Perth, Australia.
Eva M GamperHealth Outcomes Research Unit, University Hospital of Psychiatry II, Medial University of Innsbruck, 6020, Innsbruck, Austria. eva.gamper@tirol-kliniken.at.ORCID http://orcid.org/0000-0002-1700-4054
EORTC Quality of Life Group

Funding

European Organisation for Research and Treatment of Cancer #12/2016
6 · The paper itself

Abstract

backgroundThe Quality of Life Utility - Core 10 Dimensions (QLU-C10D) is a disease-specific preference-based measure (PBM) designed to obtain health state utility values from patients with cancer. Previously, satisfactory psychometric properties were established from retrospective trial analyses using clinical anchors. This study aimed to validate the QLU-C10D against two generic PBMs in a prospective sample of Austrian and French patients with cancer using patient-reported anchors.

methodsPatients completed the European Organisation of Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire - Core 30 (QLQ-C30), EQ-5D-5L and Short Form 36 (SF-36) at study baseline (any time during anti-cancer treatment) plus a follow-up assessment 3-6 months later. Sociodemographic and clinical characteristics were assessed. QLU-C10D and SF-6Dv2 utilities were calculated from QLQ-C30 and SF-36 data, respectively. German, French and UK value sets were applied for all three PBMs. Floor and ceiling effects were assessed. Known-group validity (independent t-test) and responsiveness (paired t-tests) were assessed respectively by ability to detect health status differences and changes over time according to patient-rated overall quality of life/health perception assessed by the QLQ-C30 Global Health Status scale, the EQ-5D-5L VAS and the SF-36 General Health scale.

resultsA total of 465 patients were included in the analysis. QLU-C10D index scores (intra-class correlation) and domains (Pearson) were correlated with EQ-5D-5L and Short-Form Six Dimensions (SF-6Dv2) conceptual counterparts. Correlation coefficients for the index scores of QLU-C10D and the generic PBMs ranged from 0.63 to 0.81. The QLU-C10D detected statistically significant differences between groups at baseline in 100% of tests performed (n = 27). For changes over time, QLU-C10D detected expected effects in 68% of cases (n = 29). In comparison with the generic PBMs, QLU-C10D detected differences and changes with a higher statistical efficiency in 76% of cases (77 of 102).

conclusionsThe QLU-C10D is a fit-for-purpose ready-to-use PBM to estimate health state utilities of patients with cancer. This study adds to evidence that QLU-C10D has appropriate psychometric properties and appears to have higher statistical efficiency than generic PBMs in cancer.

Indexed as

NeoplasmsPatient PreferenceQuality of LifeAdultAgedAustriaFemaleFollow-Up StudiesFranceHealth StatusHumansMaleMiddle AgedProspective StudiesPsychometricsSurveys and Questionnaires

Identifiers

PMID40287928
PMCPMC12255563

What OpenQuestion holds

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Registered trials

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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.