Computerized adaptive test (CAT) methods, based on item response theory (IRT), enable a patient-reported outcome instrument to be adapted to the individual patient while maintaining direct comparability of scores. The EORTC Quality of Life Group is developing a CAT version of the widely used EORTC QLQ-C30. We present the development and psychometric validation of the item pool for the first of the scales, physical functioning (PF).
Initial developments (including literature search and patient and expert evaluations) resulted in 56 candidate items. Responses to these items were collected from 1,176 patients with cancer from Denmark, France, Germany, Italy, Taiwan, and the United Kingdom. The items were evaluated with regard to psychometric properties.
Evaluations showed that 31 of the items could be included in a unidimensional IRT model with acceptable fit and good content coverage, although the pool may lack items at the upper extreme (good PF). There were several findings of significant differential item functioning (DIF). However, the DIF findings appeared to have little impact on the PF estimation.
We have established an item pool for CAT measurement of PF and believe that this CAT instrument will clearly improve the EORTC measurement of PF.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
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.
Fayers, P., & Bottomley, A. (2002). Quality of life research within the EORTC-the EORTC QLQ-C30. European Organisation for research and treatment of cancer. European Journal of Cancer, 38(Suppl 4), S125–S133.
Garratt, A., Schmidt, L., Mackintosh, A., & Fitzpatrick, R. (2002). Quality of life measurement: Bibliographic study of patient assessed health outcome measures. British Medical Journal, 324(7351), 1417–1419.
Fayers, P. M., Aaronson, N. K., Bjordal, K., Groenvold, M., Curran, D., & Bottomley, A. (2001). The EORTC QLQ-C30 scoring manual. Brussels: European Organisation for Research and Treatment of Cancer.
Wainer, H. (2000). Computerized adaptive testing: A primer. Mahwah, NJ: Lawrence Erlbaum.
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.
Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park: Sage.
van der Linden, W. J., & Hambleton, R. K. (1997). Handbook of modern item response theory. Berlin: Springer.
Choi, S. W., Cook, K. F., & Dodd, B. G. (1997). Parameter recovery for the partial credit model using MULTILOG. Journal of Outcome Measurement, 1(2), 114–142.
Fayers, P. M. (2007). Applying item response theory and computer adaptive testing: The challenges for health outcomes assessment. Quality of Life Research, 16(Suppl 1), 187–194.
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.
Muthen, L. K., & Muthen, B. O. (2002). Mplus user’s guide. Los Angeles, CA: Muthen & Muthen.
Cattell, R. B. (1966). Scree test for number of factors. Multivariate Behavioral Research, 1(2), 245–276.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.
Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press.
Bjorner, J. B., Kosinski, M., & Ware, J. E., Jr. (2003). Calibration of an item pool for assessing the burden of headaches: An application of item response theory to the headache impact test (HIT). Quality of Life Research, 12(8), 913–933.
Fliege, H., Becker, J., Walter, O. B., Bjorner, J. B., Klapp, B. F., & Rose, M. (2005). Development of a computer-adaptive test for depression (D-CAT). Quality of Life Research, 14(10), 2277–2291.
Junker, B. W., & Sijtsma, K. (2000). Latent and manifest monotonicity in item response models. Applied Psychological Measurement, 24(1), 63–79.
Muraki, E. (1997). A generalized partial credit model. In W. J. van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 153–168). Berlin: Springer.
Masters, G. N., & Wright, B. D. (1997). The partial credit model. In W. V. D. Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 101–121). Berlin: Springer.
Samejima, F. (1997). Graded response model. In W. V. D. Linden, & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100).
Maydeuolivares, A., Drasgow, F., & Mead, A. D. (1994). Distinguishing among parametric item response models for polychotomous ordered data. Applied Psychological Measurement, 18(3), 245–256.
Hanson, B. A. (2009). IRT command language (ICL) program http://www.b-a-h.com/software/irt/icl. Accessed June 2009.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B-Methodological, 39, 1–22.
Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46(4), 443–459.
French, A. W., & Miller, T. R. (1996). Logistic regression and its use in detecting differential item functioning in polytomous items. Journal of Educational Measurement, 33(3), 315–332.
Petersen, M. A., Groenvold, M., Bjorner, J. B., Aaronson, N. K., Conroy, T., Cull, A., et al. (2003). Use of differential item functioning analysis to assess the equivalence of translations of a questionnaire. Quality of Life Research, 12(4), 373–385.
Bjorner, J. B., Kreiner, S., Ware, J. E., Damsgaard, M. T., & Bech, P. (1998). Differential item functioning in the Danish translation of the SF-36. Journal of Clinical Epidemiology, 51(11), 1189–1202.
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 Educational Research, 45(4), 353–376.
Nagelkerke, N. J. D. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78(3), 691–692.
Scott, N. W., Fayers, P. M., Aaronson, N. K., Bottomley, A., de Graeff, A., Groenvold, M., et al. (2009). The practical impact of differential item functioning analyses in a health-related quality of life instrument. Quality of Life Research, 18(8), 1125–1130.
Hart, D. L., Deutscher, D., Crane, P. K., & Wang, Y. C. (2009). Differential item functioning was negligible in an adaptive test of functional status for patients with knee impairments who spoke English or Hebrew. Quality of Life Research, 18(8), 1067–1083.
SAS Institute Inc. (2004). SAS/STAT ® 9.1 user’s guide. Cary, NC: SAS Institute Inc.
Scott, N. W., Fayers, P. M., Bottomley, A., Aaronson, N. K., de Graeff, A., Groenvold, M., et al. (2006). Comparing translations of the EORTC QLQ-C30 using differential item functioning analyses. Quality of Life Research, 15(6), 1103–1115.
Revicki D. A., Chen, W. H., Harnam, N., Cook, K. F., Amtmann, D., Callahan, L. F., et al. (2009). Development and psychometric analysis of the PROMIS pain behavior item bank. Pain.
Helbostad, J. L., Holen, J. C., Jordhoy, M. S., Ringdal, G. I., Oldervoll, L., & Kaasa, S. (2009). A first step in the development of an international self-report instrument for physical functioning in palliative cancer care: A systematic literature review and an expert opinion evaluation study. Journal of Pain and Symptom Management, 37(2), 196–205.
Haley, S. M., Ni, P., Hambleton, R. K., Slavin, M. D., & Jette, A. M. (2006). Computer adaptive testing improved accuracy and precision of scores over random item selection in a physical functioning item bank. Journal of Clinical Epidemiology, 59(11), 1174–1182.
Haley, S. M., Fragala-Pinkham, M. A., Dumas, H. M., Ni, P., Gorton, G. E., Watson, K., et al. (2009). Evaluation of an item bank for a computerized adaptive test of activity in children with cerebral palsy. Physical Therapy, 89(6), 589–600.
Hart, D. L., Wang, Y. C., Stratford, P. W., & Mioduski, J. E. (2008). Computerized adaptive test for patients with foot or ankle impairments produced valid and responsive measures of function. Quality of Life Research, 17(8), 1081–1091.
Hart, D. L., Cook, K. F., Mioduski, J. E., Teal, C. R., & Crane, P. K. (2006). Simulated computerized adaptive test for patients with shoulder impairments was efficient and produced valid measures of function. Journal of Clinical Epidemiology, 59(3), 290–298.
Hart, D. L., Mioduski, J. E., Werneke, M. W., & Stratford, P. W. (2006). Simulated computerized adaptive test for patients with lumbar spine impairments was efficient and produced valid measures of function. Journal of Clinial Epidemiology., 59(9), 947–956.
Fries, J. F., Cella, D., Rose, M., Krishnan, E., & Bruce, B. (2009). Progress in assessing physical function in arthritis: PROMIS short forms and computerized adaptive testing. Journal of Rheumatology, 36(9), 2061–2066.
Rose, M., Bjorner, J. B., Becker, J., Fries, J. F., & Ware, J. E. (2008). Evaluation of a preliminary physical function item bank supports the expected advantages of the patient-reported outcomes measurement information system (PROMIS). Journal of Clinical Epidemiology, 61(1), 17–33.
The study was funded by grants from the EORTC Quality of Life Group. National Taiwan University, grant National Science Council, Taiwan, No. 95-2314-B-002-266-MY2, 97-2314-B-002-020-MY3.
This study is conducted on behalf of the EORTC Quality of Life Group.
About this article
Cite this article
Petersen, M.A., Groenvold, M., Aaronson, N.K. et al. Development of computerized adaptive testing (CAT) for the EORTC QLQ-C30 physical functioning dimension. Qual Life Res 20, 479–490 (2011). https://doi.org/10.1007/s11136-010-9770-x
- Computerized adaptive test
- EORTC QLQ-C30
- Item banking
- Item response theory
- Physical functioning
- Quality of life