Evidence map›Paper›PMID 38483665›Full record

ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2024

The EORTC QLU-C10D is a valid cancer-specific preference-based measure for cost-utility and health technology assessment in the Netherlands.

Micha J Pilz, Simon Seyringer, Lára R Hallsson, Andrew Bottomley, Femke Jansen, Madeleine T King, Richard Norman, Marianne J Rutten, Irma M Verdonck-de Leeuw, Peter D Siersema and 1 more

Abstract read
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Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. 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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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

11 authors.

Micha J PilzUniversity Hospital of Psychiatry II, Medical University Innsbruck, Innsbruck, Austria.
Simon SeyringerDepartment of Nuclear Medicine, Medical University of Innsbruck, 6020, Innsbruck, Austria.
Lára R HallssonInstitute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, UMIT TIROL - University for Health Sciences and Technology, Hall, I.T., Austria.
Andrew BottomleyQuality of Life Department, European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
Femke JansenDepartment Otolaryngology-Head and Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
Madeleine T KingSchool of Psychology, University of Sydney, New South Wales, Australia.
Richard NormanSchool of Population Health, Curtin University, Perth, WA, Australia.
Marianne J RuttenCenter of Gynaecologic Oncology Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Irma M Verdonck-de LeeuwDepartment Otolaryngology-Head and Neck Surgery, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, Amsterdam, The Netherlands.
Peter D SiersemaDepartment of Gastroenterology and Hepatology, Radboud University Medical Center, Nijmegen, The Netherlands.
Eva Maria GamperUniversity Hospital of Psychiatry II, Medical University Innsbruck, Innsbruck, Austria. eva.gamper@tirol-kliniken.at.

Funding

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

Abstract

backgroundCost-utility analysis typically relies on preference-based measures (PBMs). While generic PBMs are widely used, disease-specific PBMs can capture aspects relevant for certain patient populations. Here the EORTC QLU-C10D, a cancer-specific PBM based on the QLQ-C30, is validated using Dutch trial data with the EQ-5D-3L as a generic comparator measure.

methodsWe retrospectively analysed data from four Dutch randomised controlled trials (RCTs) comprising the EORTC QLQ-C30 and the EQ-5D-3L. Respective Dutch value sets were applied. Correlations between the instruments were calculated for domains and index scores. Bland-Altman plots and intra-class correlations (ICC) displayed agreement between the measures. Independent and paired t-tests, effect sizes and relative validity indices were used to determine the instruments' performance in detecting clinically known-group differences and health changes over time.

resultsWe analysed data from 602 cancer patients from four different trials. In overall, the EORTC QLU-C10D showed good relative validity with the EQ-5D-3L as a comparator (correlations of index scores r = 0.53-0.75, ICCs 0.686-0.808, conceptually similar domains showed higher correlations than dissimilar domains). Most importantly, it detected 63% of expected clinical group differences and 50% of changes over time in patients undergoing treatment. Both instruments showed poor performance in survivors. Detection rate and measurement efficiency were clearly higher for the QLU-C10D than for the EQ-5D-3L.

conclusionsThe Dutch EORTC QLU-C10D showed good comparative validity in patients undergoing treatment. Our results underline the benefit that can be achieved by using a cancer-specific PBM for generating health utilities for cancer patients from a measurement perspective.

Indexed as

Cost-Benefit AnalysisNeoplasmsQuality of LifeTechnology Assessment, BiomedicalAdultAgedFemaleHumansMaleMiddle AgedNetherlandsPatient PreferenceQuality-Adjusted Life YearsRandomized Controlled Trials as TopicReproducibility of ResultsRetrospective StudiesCancer-specific preference-based measureEORTC QLU-C10DEQ-5D-3LResponsivenessValidity

Identifiers

PMID38483665
PMCPMC11512862

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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.