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Deriving a preference-based utility measure for cancer patients from the European Organisation for the Research and Treatment of Cancer's Quality of Life Questionnaire C30: a confirmatory versus exploratory approach

Authors Costa D, Aaronson N, Fayers P, Grimison P, Janda M, Pallant J, Rowen D, Velikova G, Viney R, Young T, King M

Received 3 June 2014

Accepted for publication 18 July 2014

Published 6 November 2014 Volume 2014:5 Pages 119—129


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Daniel SJ Costa,1 Neil K Aaronson,2 Peter M Fayers,3,4 Peter S Grimison,5,6 Monika Janda,7 Julie F Pallant,8 Donna Rowen,9 Galina Velikova,10 Rosalie Viney,11 Tracey A Young,9 Madeleine T King1

On behalf of the MAUCa Consortium

1Psycho-oncology Co-operative Research Group, University of Sydney, Sydney, NSW, Australia; 2Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands; 3Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK; 4Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway; 5Chris O'Brien Lifehouse, 6Sydney Medical School, University of Sydney, Sydney, NSW, 7School of Public Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, 8Rural Health Academic Centre, University of Melbourne, Shepparton, VIC, Australia; 9School of Health and Related Research, University of Sheffield, Sheffield; 10University of Leeds, St James's Institute of Oncology, Leeds, UK; 11Centre for Health Economics Research and Evaluation, University of Technology, Sydney, NSW, Australia

Background: Multi attribute utility instruments (MAUIs) are preference-based measures that comprise a health state classification system (HSCS) and a scoring algorithm that assigns a utility value to each health state in the HSCS. When developing a MAUI from a health-related quality of life (HRQOL) questionnaire, first a HSCS must be derived. This typically involves selecting a subset of domains and items because HRQOL questionnaires typically have too many items to be amendable to the valuation task required to develop the scoring algorithm for a MAUI. Currently, exploratory factor analysis (EFA) followed by Rasch analysis is recommended for deriving a MAUI from a HRQOL measure.
Aim: To determine whether confirmatory factor analysis (CFA) is more appropriate and efficient than EFA to derive a HSCS from the European Organisation for the Research and Treatment of Cancer's core HRQOL questionnaire, Quality of Life Questionnaire (QLQ-C30), given its well-established domain structure.
Methods: QLQ-C30 (Version 3) data were collected from 356 patients receiving palliative radiotherapy for recurrent/metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter informed by the established QLQ-C30 structure and views of both patients and clinicians on which are the most relevant items. Dimensions determined by EFA or CFA were then subjected to Rasch analysis.
Results: CFA results generally supported the proposed QLQ-C30 structure (comparative fit index =0.99, Tucker–Lewis index =0.99, root mean square error of approximation =0.04). EFA revealed fewer factors and some items cross-loaded on multiple factors. Further assessment of dimensionality with Rasch analysis allowed better alignment of the EFA dimensions with those detected by CFA.
Conclusion: CFA was more appropriate and efficient than EFA in producing clinically interpretable results for the HSCS for a proposed new cancer-specific MAUI. Our findings suggest that CFA should be recommended generally when deriving a preference-based measure from a HRQOL measure that has an established domain structure.

Keywords: multi attribute utility instrument, health state classification system, confirmatory factor analysis, exploratory factor analysis, European Organisation for the Research and Treatment of Cancer QLQ-C30

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