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Qual Life Res. 2016 Mar;25(3):625-36. doi: 10.1007/s11136-015-1217-y. Epub 2016 Jan 20.

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

Author information

1
Psycho-Oncology Cooperative Research Group (PoCoG), School of Psychology, Faculty of Science, University of Sydney, Sydney, NSW, Australia. madeleine.king@sydney.edu.au.
2
Central Clinical School, Sydney Medical School, Faculty of Medicine, University of Sydney, Sydney, NSW, Australia. madeleine.king@sydney.edu.au.
3
Psycho-Oncology Cooperative Research Group (PoCoG), School of Psychology, Faculty of Science, University of Sydney, Sydney, NSW, Australia.
4
Division of Psychosocial Research & Epidemiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
5
Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, Sheffield, South Yorkshire, UK.
6
Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
7
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK.
8
Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
9
Chris O'Brien Lifehouse, Sydney Medical School, University of Sydney, Sydney, NSW, Australia.
10
School of Public Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia.
11
Department of Psychiatry and Psychotherapy, Innsbruck Medical University, Innsbruck, Austria.
12
School of Public Health, Curtin University, Perth, WA, Australia.
13
Centre for Health Economics Research and Evaluation (CHERE), University of Technology Sydney (UTS), Sydney, NSW, Australia.
14
Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA.
15
Leeds Institute of Cancer and Pathology, University of Leeds, St James's University Hospital, Leeds, UK.

Abstract

PURPOSE:

To 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.

METHODS:

The 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.

RESULTS:

CFA 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.

CONCLUSIONS:

The 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.

KEYWORDS:

Cancer; Multi-attribute utility instrument; QLQ-C30; Quality of life; Utility

PMID:
26790428
DOI:
10.1007/s11136-015-1217-y
[Indexed for MEDLINE]
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