Deriving a preference-based utility measure for cancer patients from the EORTC QLQ-C30: a confirmatory versus exploratory approach

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

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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 QLQC30 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.
Original languageEnglish
Pages (from-to)119-129
Number of pages11
JournalPatient Related Outcome Measures
Early online date6 Nov 2014
Publication statusPublished - 2014

Bibliographical note

The Multi-Attribute Utility in Cancer (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, as outlined above: John
Brazier, David Cella, Stein Kaasa, Georg Kemmler, Helen McTaggart-Cowan, Richard Norman, Stuart Peacock, Simon Pickard, Neil Scott, Martin Stockler, and Deborah Street. This research was supported by a National Health and Medical Research Council (NHMRC; Australia) Project Grant (632662). Monika Janda is supported by an NHMRC career development award 1045247. Professor King is supported by
the Australian Government through Cancer Australia.


  • 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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