Comparing higher order models for the EORTC QLQ-C30

Chad M. Gundy, Peter M Fayers, Mogens Groenvold, Morten Aa. Petersen, Neil W Scott, Mirjam A. G. Sprangers, Galina Velikova, Neil K. Aaronson

Research output: Contribution to journalArticle

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Abstract

PURPOSE: To investigate the statistical fit of alternative higher order models for summarizing the health-related quality of life profile generated by the EORTC QLQ-C30 questionnaire.
METHODS: A 50% random sample was drawn from a dataset of more than 9,000 pre-treatment QLQ-C30 v 3.0 questionnaires completed by cancer patients from 48 countries, differing in primary tumor site and disease stage. Building on a "standard" 14-dimensional QLQ-C30 model, confirmatory factor analysis was used to compare 6 higher order models, including a 1-dimensional (1D) model, a 2D "symptom burden and function" model, two 2D "mental/physical" models, and two models with a "formative" (or "causal") formulation of "symptom burden," and "function."
RESULTS: All of the models considered had at least an "adequate" fit to the data: the less restricted the model, the better the fit. The RMSEA fit indices for the various models ranged from 0.042 to 0.061, CFI's 0.90-0.96, and TLI's from 0.96 to 0.98. All chi-square tests were significant. One of the Physical/Mental models had fit indices superior to the other models considered.
CONCLUSIONS: The Physical/Mental health model had the best fit of the higher order models considered, and enjoys empirical and theoretical support in comparable instruments and applications.
Original languageEnglish
Pages (from-to)1607-1617
Number of pages11
JournalQuality of Life Research
Volume21
Issue number9
Early online date21 Dec 2011
DOIs
Publication statusPublished - Nov 2012

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Chi-Square Distribution
Statistical Factor Analysis
Neoplasms
Mental Health
Quality of Life
Surveys and Questionnaires
Therapeutics
chemotactic factor inactivator
Datasets

Keywords

  • health-related quality of life
  • confirmatory factor analysis
  • higher order factor
  • EORTC QLQ-C30

Cite this

Gundy, C. M., Fayers, P. M., Groenvold, M., Petersen, M. A., Scott, N. W., Sprangers, M. A. G., ... Aaronson, N. K. (2012). Comparing higher order models for the EORTC QLQ-C30. Quality of Life Research, 21(9), 1607-1617. https://doi.org/10.1007/s11136-011-0082-6

Comparing higher order models for the EORTC QLQ-C30. / Gundy, Chad M.; Fayers, Peter M; Groenvold, Mogens; Petersen, Morten Aa.; Scott, Neil W; Sprangers, Mirjam A. G.; Velikova, Galina; Aaronson, Neil K.

In: Quality of Life Research, Vol. 21, No. 9, 11.2012, p. 1607-1617.

Research output: Contribution to journalArticle

Gundy, CM, Fayers, PM, Groenvold, M, Petersen, MA, Scott, NW, Sprangers, MAG, Velikova, G & Aaronson, NK 2012, 'Comparing higher order models for the EORTC QLQ-C30', Quality of Life Research, vol. 21, no. 9, pp. 1607-1617. https://doi.org/10.1007/s11136-011-0082-6
Gundy, Chad M. ; Fayers, Peter M ; Groenvold, Mogens ; Petersen, Morten Aa. ; Scott, Neil W ; Sprangers, Mirjam A. G. ; Velikova, Galina ; Aaronson, Neil K. / Comparing higher order models for the EORTC QLQ-C30. In: Quality of Life Research. 2012 ; Vol. 21, No. 9. pp. 1607-1617.
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N2 - PURPOSE: To investigate the statistical fit of alternative higher order models for summarizing the health-related quality of life profile generated by the EORTC QLQ-C30 questionnaire. METHODS: A 50% random sample was drawn from a dataset of more than 9,000 pre-treatment QLQ-C30 v 3.0 questionnaires completed by cancer patients from 48 countries, differing in primary tumor site and disease stage. Building on a "standard" 14-dimensional QLQ-C30 model, confirmatory factor analysis was used to compare 6 higher order models, including a 1-dimensional (1D) model, a 2D "symptom burden and function" model, two 2D "mental/physical" models, and two models with a "formative" (or "causal") formulation of "symptom burden," and "function." RESULTS: All of the models considered had at least an "adequate" fit to the data: the less restricted the model, the better the fit. The RMSEA fit indices for the various models ranged from 0.042 to 0.061, CFI's 0.90-0.96, and TLI's from 0.96 to 0.98. All chi-square tests were significant. One of the Physical/Mental models had fit indices superior to the other models considered. CONCLUSIONS: The Physical/Mental health model had the best fit of the higher order models considered, and enjoys empirical and theoretical support in comparable instruments and applications.

AB - PURPOSE: To investigate the statistical fit of alternative higher order models for summarizing the health-related quality of life profile generated by the EORTC QLQ-C30 questionnaire. METHODS: A 50% random sample was drawn from a dataset of more than 9,000 pre-treatment QLQ-C30 v 3.0 questionnaires completed by cancer patients from 48 countries, differing in primary tumor site and disease stage. Building on a "standard" 14-dimensional QLQ-C30 model, confirmatory factor analysis was used to compare 6 higher order models, including a 1-dimensional (1D) model, a 2D "symptom burden and function" model, two 2D "mental/physical" models, and two models with a "formative" (or "causal") formulation of "symptom burden," and "function." RESULTS: All of the models considered had at least an "adequate" fit to the data: the less restricted the model, the better the fit. The RMSEA fit indices for the various models ranged from 0.042 to 0.061, CFI's 0.90-0.96, and TLI's from 0.96 to 0.98. All chi-square tests were significant. One of the Physical/Mental models had fit indices superior to the other models considered. CONCLUSIONS: The Physical/Mental health model had the best fit of the higher order models considered, and enjoys empirical and theoretical support in comparable instruments and applications.

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