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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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 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
Volume5
Early online date6 Nov 2014
DOIs
Publication statusPublished - 2014

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Statistical Factor Analysis
Neoplasms
Quality of Life
Health
Radiotherapy
Organizations

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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Deriving a preference-based utility measure for cancer patients from the EORTC QLQ-C30 : a confirmatory versus exploratory approach. / Costa, Daniel S.J.; Aaronson, Neil K; Fayers, Peter; Grimison, Peter S; Janda, Monika; Pallant, Julie F; Rowen, Donna ; Velikova, Galina; Viney, Rosalie; Young, Tracey A ; King, Madeleine T.

In: Patient Related Outcome Measures, Vol. 5, 2014, p. 119-129.

Research output: Contribution to journalArticle

Costa, DSJ, Aaronson, NK, Fayers, P, Grimison, PS, Janda, M, Pallant, JF, Rowen, D, Velikova, G, Viney, R, Young, TA & King, MT 2014, 'Deriving a preference-based utility measure for cancer patients from the EORTC QLQ-C30: a confirmatory versus exploratory approach', Patient Related Outcome Measures, vol. 5, pp. 119-129. https://doi.org/10.2147/PROM.S68776
Costa, Daniel S.J. ; Aaronson, Neil K ; Fayers, Peter ; Grimison, Peter S ; Janda, Monika ; Pallant, Julie F ; Rowen, Donna ; Velikova, Galina ; Viney, Rosalie ; Young, Tracey A ; King, Madeleine T. / Deriving a preference-based utility measure for cancer patients from the EORTC QLQ-C30 : a confirmatory versus exploratory approach. In: Patient Related Outcome Measures. 2014 ; Vol. 5. pp. 119-129.
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title = "Deriving a preference-based utility measure for cancer patients from the EORTC QLQ-C30: a confirmatory versus exploratory approach",
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 itemsto 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.",
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author = "Costa, {Daniel S.J.} and Aaronson, {Neil K} and Peter Fayers and Grimison, {Peter S} and Monika Janda and Pallant, {Julie F} and Donna Rowen and Galina Velikova and Rosalie Viney and Young, {Tracey A} and King, {Madeleine T.}",
note = "Acknowledgments 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.",
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doi = "10.2147/PROM.S68776",
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pages = "119--129",
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T1 - Deriving a preference-based utility measure for cancer patients from the EORTC QLQ-C30

T2 - a confirmatory versus exploratory approach

AU - Costa, Daniel S.J.

AU - Aaronson, Neil K

AU - Fayers, Peter

AU - Grimison, Peter S

AU - Janda, Monika

AU - Pallant, Julie F

AU - Rowen, Donna

AU - Velikova, Galina

AU - Viney, Rosalie

AU - Young, Tracey A

AU - King, Madeleine T.

N1 - Acknowledgments 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.

PY - 2014

Y1 - 2014

N2 - 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 itemsto 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.

AB - 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 itemsto 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.

KW - multi attribute utility instrument

KW - health state classification system

KW - confirmatory factor analysis

KW - exploratory factor analysis

KW - European Organisation for the Research and Treatment of Cancer QLQ-C30

U2 - 10.2147/PROM.S68776

DO - 10.2147/PROM.S68776

M3 - Article

VL - 5

SP - 119

EP - 129

JO - Patient Related Outcome Measures

JF - Patient Related Outcome Measures

SN - 1179-271X

ER -