Psychometric evaluation of the EORTC computerized adaptive test (CAT) fatigue item pool
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Fatigue is one of the most common symptoms associated with cancer and its treatment. To obtain a more precise and flexible measure of fatigue, the EORTC Quality of Life Group has developed a computerized adaptive test (CAT) measure of fatigue. This is part of an ongoing project developing a CAT version of the widely used EORTC QLQ-C30 questionnaire.
Based on the literature search and evaluations by experts and patients, 41 new fatigue items were developed (in addition to the three QLQ-C30 fatigue items). Psychometric properties of the items, including evaluations of dimensionality, fit to item response theory (IRT) model, and differential item functioning (DIF), were assessed in an international sample of cancer patients.
Responses were obtained from 1,321 cancer patients coming from eight countries. Factor analysis showed that 37 of the items could be included in a unidimensional model (RMSEA = 0.098, TLI = 0.995, CFI = 0.920). Of the 37 items, two were deleted because of poor fit to the IRT model forming the basis for the CAT, and one because of DIF between cancer sites.
We have established a 34-item fatigue bank allowing for more precise and flexible measurement of fatigue, while still being backward compatible with the QLQ-C30 fatigue scale.
KeywordsComputerized adaptive test EORTC QLQ-C30 Fatigue Item banking Item response theory Quality of life
Computerized adaptive test
Comparative Fit Index
Differential item functioning
European Organisation for Research and Treatment of Cancer
Generalized partial credit model
Health-related quality of life
Item response theory
Patient Reported Outcome Measurement Information System
Quality of Life Questionnaire Core 30
Root mean square error of approximation
The study was funded by grants from the EORTC Quality of Life Group. The authors would like to thank the patients responding to our items and our collaborators for collecting these essential patient responses.
- 1.Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85(5), 365–376.PubMedCrossRefGoogle Scholar
- 3.Wainer, H. (2000). Computerized Adaptive testing: A primer (2nd ed.). Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc.Google Scholar
- 10.The Patient-Reported Outcomes Measurement Information System (PROMIS). http://www.nihpromis.org. Accessed March 2011.
- 11.Garcia, S. F., Cella, D., Clauser, S. B., Flynn, K. E., Lad, T., Lai, J. S., et al. (2007). Standardizing patient-reported outcomes assessment in cancer clinical trials: A patient-reported outcomes measurement information system initiative. Journal of Clinical Oncology, 25(32), 5106–5112.PubMedCrossRefGoogle Scholar
- 13.Vogelzang, N. J., Breitbart, W., Cella, D., Curt, G. A., Groopman, J. E., Horning, S. J., et al. (1997). Patient, caregiver, and oncologist perceptions of cancer-related fatigue: results of a tripart assessment survey. The Fatigue Coalition. Seminars in Hematology, 34(3 Suppl 2), 4–12.PubMedGoogle Scholar
- 15.Petersen, M. Aa., Groenvold, M., Aaronson, N. K., Chie, W.-C., Conroy, T., Costantini, A., et al. (2010). Development of computerised adaptive testing (CAT) for the EORTC QLQ-C30 dimensions—General approach and initial results for physical functioning. European Journal of Cancer, 46, 1352–1358.PubMedCrossRefGoogle Scholar
- 19.Muraki, E., & Bock, R. D. (1996). PARSCALE—IRT based test scoring and item analysis for graded open-ended exercises and performance tasks. Chicago: Scientific Software International, Inc.Google Scholar
- 20.Muthen, L. K., & Muthen, B. O. (2002). Mplus user’s guide (2nd ed.). Los Angeles, CA: Muthen & Muthen.Google Scholar
- 23.Kline, R. B. (2005). Principles and practice of structural equation modelling (2nd ed.). New York: The Guilford Press.Google Scholar
- 27.Samejima, F. (1997). Graded response model. In: Linden, W. V. D. & Hambleton, R. K. (Eds.), Handbook of modern item response theory (pp. 85–100). Berlin: Springer.Google Scholar
- 29.Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). New Jersey: Lawrence Erlbaum Associates, Inc.Google Scholar
- 30.Wright, B. D., & Linacre, J. M. (1994). Reasonable mean-square fit values. Rasch Measurement Transactions, 8(3), 370.Google Scholar
- 31.Holland, P. W., & Wainer, H. (1993). Differential item functioning. Hilsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
- 35.Gierl, M. J., Rogers, W. T., & Klinger, D. A. (1999). Using statistical and judgmental reviews to identify and interpret translation differential item functioning. Alberta Journal of Education and Research, 45(4), 353–376.Google Scholar