Evidence map›Paper›PMID 26790428›Full record

ArticleQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation2016

QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30.

M T King, D S J Costa, N K Aaronson, J E Brazier, D F Cella, P M Fayers, P Grimison, M Janda, G Kemmler, R Norman and 5 more

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In one paragraph

Article in Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 72 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 2 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

72 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Health-related quality of life in patients with extremity bone sarcoma after surgical treatment: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024
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  3. Cancer-specific utility: clinical validation of the EORTC QLU-C10D in patients with glioblastoma.The European journal of health economics : HEPAC : health economics in prevention and care · 2025
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  15. Cancer-specific or generic preference-based measures in oncology? A validity comparison across six commonly diagnosed cancers.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026
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  17. Comparing EQ-5D-5L and SF-6Dv2 utilities with QLU-C10D utilities in hematologic cancer patients.The European journal of health economics : HEPAC : health economics in prevention and care · 2026
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  19. Developing a patient-based utility value set for the EORTC QLU-C10D using the OPUF method in Chinese cancer patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025
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12 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

15 authors.

M T KingPsycho-Oncology Cooperative Research Group (PoCoG), School of Psychology, Faculty of Science, University of Sydney, Sydney, NSW, Australia. madeleine.king@sydney.edu.au.
D S J CostaPsycho-Oncology Cooperative Research Group (PoCoG), School of Psychology, Faculty of Science, University of Sydney, Sydney, NSW, Australia.
N K AaronsonDivision of Psychosocial Research & Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
J E BrazierHealth Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, South Yorkshire, UK.
D F CellaDepartment of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
P M FayersInstitute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK.
P GrimisonChris O'Brien Lifehouse, Sydney Medical School, University of Sydney, Sydney, NSW, Australia.
M JandaSchool of Public Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia.
G KemmlerDepartment of Psychiatry and Psychotherapy, Innsbruck Medical University, Innsbruck, Austria.
R NormanSchool of Public Health, Curtin University, Perth, WA, Australia.
A S PickardDepartment of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA.
D RowenHealth Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, South Yorkshire, UK.
G VelikovaLeeds Institute of Cancer and Pathology, University of Leeds, St James's University Hospital, Leeds, UK.
T A YoungHealth Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, South Yorkshire, UK.
R VineyCentre for Health Economics Research and Evaluation (CHERE), University of Technology Sydney (UTS), Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo derive a health state classification system (HSCS) from the cancer-specific quality of life questionnaire, the EORTC QLQ-C30, as the basis for a multi-attribute utility instrument.

methodsThe conceptual model for the HSCS was based on the established domain structure of the QLQ-C30. Several criteria were considered to select a subset of dimensions and items for the HSCS. Expert opinion and patient input informed a priori selection of key dimensions. Psychometric criteria were assessed via secondary analysis of a pooled dataset comprising HRQOL and clinical data from 2616 patients from eight countries and a range of primary cancer sites, disease stages, and treatments. We used confirmatory factor analysis (CFA) to assess the conceptual model's robustness and generalisability. We assessed item floor effects (>75 % observations at lowest score), disordered item response thresholds, coverage of the latent variable and differential item function using Rasch analysis. We calculated effect sizes for known group comparisons based on disease stage and responsiveness to change. Seventy-nine cancer patients assessed the relative importance of items within dimensions.

resultsCFA supported the conceptual model and its generalisability across primary cancer sites. After considering all criteria, 12 items were selected representing 10 dimensions: physical functioning (mobility), role functioning, social functioning, emotional functioning, pain, fatigue, sleep, appetite, nausea, bowel problems.

conclusionsThe HSCS created from QLQ-C30 items is known as the EORTC Quality of Life Utility Measure-Core 10 dimensions (QLU-C10D). The next phase of the QLU-C10D's development involves valuation studies, currently planned or being conducted across the globe.

Indexed as

Health StatusPhysical FitnessSurveys and QuestionnairesAdultAgedFactor Analysis, StatisticalFatigueFemaleHumansMaleMiddle AgedNeoplasmsPainPsychometricsQuality of LifeReproducibility of ResultsCancerMulti-attribute utility instrumentQLQ-C30Quality of lifeUtility

Identifiers

PMID26790428

What OpenQuestion holds

Textmetadata
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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.