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.
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.


OBJECTIVE
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.


CONCLUSIONS
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.
Psycho-oncology Co-operative Research Group, University of Sydney, Sydney, NSW, Australia.
Daniel Sj
DS
Costa
Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute, Amsterdam, the Netherlands.
Neil K
NK
Aaronson
Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK ; Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway.
Peter M
PM
Fayers
Chris O'Brien Lifehouse, University of Sydney, Sydney, NSW, Australia ; Sydney Medical School, University of Sydney, Sydney, NSW, Australia.
Peter S
PS
Grimison
School of Public Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia.
Monika
M
Janda
Rural Health Academic Centre, University of Melbourne, Shepparton, VIC, Australia.
Julie F
JF
Pallant
School of Health and Related Research, University of Sheffield, Sheffield, UK.
Donna
D
Rowen
University of Leeds, St James's Institute of Oncology, Leeds, UK.
Galina
G
Velikova
Centre for Health Economics Research and Evaluation, University of Technology, Sydney, NSW, Australia.
Rosalie
R
Viney
School of Health and Related Research, University of Sheffield, Sheffield, UK.
Tracey A
TA
Young
Psycho-oncology Co-operative Research Group, University of Sydney, Sydney, NSW, Australia.
Madeleine T
MT
King
European Organisation for the Research and Treatment of Cancer QLQ-C30; confirmatory factor analysis; exploratory factor analysis; health state classification system; multi attribute utility instrument