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

Daniel Sj Costa; Neil K Aaronson; Peter M Fayers; Peter S Grimison; Monika Janda; Julie F Pallant; Donna Rowen; Galina Velikova; Rosalie Viney; Tracey A Young; Madeleine T King

doi:10.2147/PROM.S68776

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

Patient Relat Outcome Meas. 2014 Nov 6:5:119-29. doi: 10.2147/PROM.S68776. eCollection 2014.

Authors

Affiliations

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

Abstract

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: 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.