Can pain and function be distinguished in the Oxford Knee Score in a meaningful way? An exploratory and confirmatory factor analysis
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The purpose of this paper was to examine if pain and functioning can be distinguished in the Oxford Knee Score (OKS) in a meaningful way. This was done by (1) conducting exploratory factor analysis to explore the factorial structure of the OKS and (2) conducting confirmatory factor analysis to examine whether a two-factor solution is superior to a one-factor solution.
Secondary data analysis of four independent datasets containing OKS scores on 161,973 patients was performed. Four independent datasets contained data on: (1) 156, 788 patients from the NHS HES/PROMS dataset, (2) 2,405 consecutive patients from the South West London Elective Operating Centre, (3) 2,353 patients enrolled in the Knee Arthroplasty Trial and (4) 427 consecutive patients listed for knee replacement surgery at the Nuffield Orthopaedic Centre in Oxford.
Factor extraction methods suggested that, depending on the method employed, both one- and two-factor solutions are meaningful. Overall and in each data set some cross-loading occurred and item loadings were consistent across two factors. On confirmatory factor analysis, both one- and two-factor models had acceptable fit indices. This allowed the creation of the ‘OKS pain component’ and the ‘OKS functional component’ subscales.
Factor analysis confirmed the original conceptual basis of the OKS but offered an option to perform additional analyses using pain and functional subscales. Further research should focus on providing further evidence on construct validity and responsiveness of the newly derived subscales.
KeywordsPatient-reported outcomes Osteoarthritis Total joint replacement Outcomes assessment
A copy of the OKS questionnaire and permission to use this measure can be acquired from Isis Innovation Ltd, the technology transfer company of the University of Oxford via website: http://www.isis-innovation.com/outcomes/index.html or email: firstname.lastname@example.org. Authors at the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences thank the NIHR Biomedical Research Unit for its support. The KAT dataset was reused with the permission of the KAT Project Management Group.
- 1.Agency for Healthcare Research and Quality (February, 2012). Facts and figures. Statistics on hospital-based care in the United States, 2009. www.hcup-us.ahrq.gov/reports/factsandfigures/2009/exhibit3_1.jsp. Accessed 11 June 2012.
- 2.National Joint Registry for England and Wales (2011). 8th annual report. Hemel Hempstead, Hertfordshire, UK.Google Scholar
- 5.Devlin, N. J., & Appleby, J. (2010). Getting the most out of PROMs. London: King’s Fund, Office of Health Economics.Google Scholar
- 8.Dawson, J., Fitzpatrick, M., Churchman, D., Verjee-Lorenz, A., & Claysonm, D. (2010). User manual for the Oxford Knee Score (OKS). Isis Innovation Limited.Google Scholar
- 10.Baker, P., Van der Meulen, J., Lewsey, J., & Gregg, P. (2007). The role of pain and function in determining patient satisfaction after total knee replacement: Data from the National Joint Registry for England and Wales. Journal of Bone and Joint Surgery. British Volume, 89(7), 893.Google Scholar
- 12.Scott, C., Howie, C., MacDonald, D., & Biant, L. (2010). Predicting dissatisfaction following total knee replacement: a prospective study of 1217 patients. Journal of Bone and Joint Surgery-British Volume, 92(9), 1253.Google Scholar
- 14.The Health and Social Care Information Centre Annual publication data quality notes library. Available at: http://www.hesonline.nhs.uk/Ease/servlet/ContentServer?siteID=1937&categoryID=1189. Accessed 26 March 2012.
- 15.Murray, D., Fitzpatrick, R., Rogers, K., Pandit, H., Beard, D., Carr, A., et al. (2007). The use of the Oxford hip and knee scores. Journal of Bone and Joint Surgery-British Volume, 89(8), 1010.Google Scholar
- 16.Bollen, K. A. (1989). Structural equations with latent variables. Wiley.Google Scholar
- 17.Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. New York: McGraw-Hill.Google Scholar
- 20.Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The bare essentials. PMPH USA Ltd.Google Scholar
- 22.Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement, 20, 141–151.Google Scholar
- 24.Ledesma, R. D., & Valero-Mora, P. (2007). Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis. Practical Assessment, Research & Evaluation, 12(2), 1–11.Google Scholar
- 28.O’connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, 32(3), 396–402.Google Scholar
- 32.Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In: Problems and solutions in human assessment 2000 (pp. 41–71). USA: Springer.Google Scholar
- 34.Maruyama, G. (1998). Basics of structural equation modelling. California: Thousand Oaks.Google Scholar
- 35.Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Testing structural equation models, 154, 136–162.Google Scholar
- 37.Stewart, A. L., & Ware, J. E. (1992). Measuring functioning and well-being: The medical outcomes study approach. USA: RAND Corporation.Google Scholar
- 38.Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7), 1–9.Google Scholar
- 41.Kline, R. B. (2010). Principles and practice of structural equation modelling. New York: Guilford Press.Google Scholar