QLU-C10D

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

M. T. King* (Corresponding Author), D. S.J. Costa, N. K. Aaronson, J. E. Brazier, D. F. Cella, P. M. Fayers, P. Grimison, M. Janda, G. Kemmler, R. Norman, A. S. Pickard, D. Rowen, G. Velikova, T. A. Young, R. Viney

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)625-636
Number of pages12
JournalQuality of Life Research
Volume25
Issue number3
Early online date20 Jan 2016
DOIs
Publication statusPublished - 1 Mar 2016

Fingerprint

Quality of Life
Health
Statistical Factor Analysis
Neoplasms
Expert Testimony
Appetite
Psychometrics
Nausea
Fatigue
Sleep
Pain
Therapeutics

Keywords

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

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

QLU-C10D : a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30. / King, M. T. (Corresponding Author); Costa, D. S.J.; Aaronson, N. K.; Brazier, J. E.; Cella, D. F.; Fayers, P. M.; Grimison, P.; Janda, M.; Kemmler, G.; Norman, R.; Pickard, A. S.; Rowen, D.; Velikova, G.; Young, T. A.; Viney, R.

In: Quality of Life Research, Vol. 25, No. 3, 01.03.2016, p. 625-636.

Research output: Contribution to journalArticle

King, MT, Costa, DSJ, Aaronson, NK, Brazier, JE, Cella, DF, Fayers, PM, Grimison, P, Janda, M, Kemmler, G, Norman, R, Pickard, AS, Rowen, D, Velikova, G, Young, TA & Viney, R 2016, 'QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30', Quality of Life Research, vol. 25, no. 3, pp. 625-636. https://doi.org/10.1007/s11136-015-1217-y
King, M. T. ; Costa, D. S.J. ; Aaronson, N. K. ; Brazier, J. E. ; Cella, D. F. ; Fayers, P. M. ; Grimison, P. ; Janda, M. ; Kemmler, G. ; Norman, R. ; Pickard, A. S. ; Rowen, D. ; Velikova, G. ; Young, T. A. ; Viney, R. / QLU-C10D : a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30. In: Quality of Life Research. 2016 ; Vol. 25, No. 3. pp. 625-636.
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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",
author = "King, {M. T.} and Costa, {D. S.J.} and Aaronson, {N. K.} and Brazier, {J. E.} and Cella, {D. F.} and Fayers, {P. M.} and P. Grimison and M. Janda and G. Kemmler and R. Norman and Pickard, {A. S.} and D. Rowen and G. Velikova and Young, {T. A.} and R. Viney",
note = "Acknowledgments The MAUCa Consortium, in addition to those named as authors, consists of the following members, all of whom made some contribution to the research reported in this paper: Stuart Peacock, Helen McTaggart-Cowan, Julie Pallant and Deborah Street. We would also like to thank the following people for their generosity in contributing data for secondary analysis: U. Abacioğlu, J. Arraras, J. Blazeby, W.-C. Chie, S. Clarke, S. Kaasa, P. Klepstad, Millennium Pharmaceuticals, K. Mystakidou, S. Peacock, R. Schwarz, N. Scott, N. Tebbutt, G. Velikova and the Australian Gastro-Intestinal Trials Group. This research was supported by a National Health and Medical Research Council (Australia) Project Grant (632662). A/Professor Janda was supported by a NHMRC career development award 1045247. Professor King was supported by the Australian Government through Cancer Australia. Funding This research was supported by a National Health and Medical Research Council (Australia) Project Grant (632662). Professor King was supported by the Australian Government through Cancer Australia. Dr. Norman was supported by a NHMRC early career research fellowship (1069732).",
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T2 - a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30

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AU - Costa, D. S.J.

AU - Aaronson, N. K.

AU - Brazier, J. E.

AU - Cella, D. F.

AU - Fayers, P. M.

AU - Grimison, P.

AU - Janda, M.

AU - Kemmler, G.

AU - Norman, R.

AU - Pickard, A. S.

AU - Rowen, D.

AU - Velikova, G.

AU - Young, T. A.

AU - Viney, R.

N1 - Acknowledgments The MAUCa Consortium, in addition to those named as authors, consists of the following members, all of whom made some contribution to the research reported in this paper: Stuart Peacock, Helen McTaggart-Cowan, Julie Pallant and Deborah Street. We would also like to thank the following people for their generosity in contributing data for secondary analysis: U. Abacioğlu, J. Arraras, J. Blazeby, W.-C. Chie, S. Clarke, S. Kaasa, P. Klepstad, Millennium Pharmaceuticals, K. Mystakidou, S. Peacock, R. Schwarz, N. Scott, N. Tebbutt, G. Velikova and the Australian Gastro-Intestinal Trials Group. This research was supported by a National Health and Medical Research Council (Australia) Project Grant (632662). A/Professor Janda was supported by a NHMRC career development award 1045247. Professor King was supported by the Australian Government through Cancer Australia. Funding This research was supported by a National Health and Medical Research Council (Australia) Project Grant (632662). Professor King was supported by the Australian Government through Cancer Australia. Dr. Norman was supported by a NHMRC early career research fellowship (1069732).

PY - 2016/3/1

Y1 - 2016/3/1

N2 - 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.

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KW - Multi-attribute utility instrument

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KW - Quality of life

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