Skip to main content
Top
Published in: Quality of Life Research 10/2011

Open Access 01-12-2011

Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients

Authors: Bellinda L. King-Kallimanis, Frans. J. Oort, Sandra Nolte, Carolyn E. Schwartz, Mirjam A. G. Sprangers

Published in: Quality of Life Research | Issue 10/2011

Login to get access

Abstract

Purpose

To illustrate how structural equation modeling (SEM) can be used for response shift detection with random measurement occasions and health state operationalized as fixed group membership (Study 1) or with fixed measurement occasions and health state operationalized as time-varying covariates (Study 2).

Methods

In Study 1, we explored seven items of the Performance Scales measuring physical and mental aspects of perceived disability of 771 stable, 629 progressive, and 1,552 relapsing MS patients. Time lags between the three measurements varied and were accounted for by introducing time since diagnosis as an exogenous variable. In Study 2, we considered the SF-12 scales measuring physical and mental components of HRQoL of 1,767 patients. Health state was accounted for by exogenous variables relapse (yes/no) and symptoms (worse/same/better).

Results

In Study 1, progressive and relapsing patients reported greater disability than stable patients but little longitudinal change. Some response shift was found with stable and relapsing patients. In Study 2, relapse and symptoms were associated with HRQoL, but no change and only little response shift was found.

Conclusions

While small response shifts were found, they had little impact on the evaluation of true change in performance and HRQoL.
Appendix
Available only for authorised users
Literature
1.
go back to reference Sprangers, M. A. G., & Schwartz, C. E. (1999). Integrating response shift into health-related quality of life research: a theoretical model. Social Science and Medicine, 48, 1507–1515.PubMedCrossRef Sprangers, M. A. G., & Schwartz, C. E. (1999). Integrating response shift into health-related quality of life research: a theoretical model. Social Science and Medicine, 48, 1507–1515.PubMedCrossRef
2.
go back to reference Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14, 587–598.PubMedCrossRef Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14, 587–598.PubMedCrossRef
3.
go back to reference Visser, M. R. M., Oort, F. J., & Sprangers, M. A. G. (2005). Methods to detect response shift in quality of life data: A convergent validity study. Quality of Life Research, 14, 629–639.PubMedCrossRef Visser, M. R. M., Oort, F. J., & Sprangers, M. A. G. (2005). Methods to detect response shift in quality of life data: A convergent validity study. Quality of Life Research, 14, 629–639.PubMedCrossRef
4.
go back to reference Oort, F. J., Visser, M. R., & Sprangers, M. A. (2009). Formal definitions of measurement bias and explanation bias clarify measurement and conceptual perspectives on response shift. Journal of Clinical Epidemiolgy, 62, 1126–1137.CrossRef Oort, F. J., Visser, M. R., & Sprangers, M. A. (2009). Formal definitions of measurement bias and explanation bias clarify measurement and conceptual perspectives on response shift. Journal of Clinical Epidemiolgy, 62, 1126–1137.CrossRef
5.
go back to reference Oort, F. J. (2005). Towards a formal definition of response shift (In reply to G.W. Donaldson). Quality of Life Research, 14, 2353–2355.PubMedCrossRef Oort, F. J. (2005). Towards a formal definition of response shift (In reply to G.W. Donaldson). Quality of Life Research, 14, 2353–2355.PubMedCrossRef
6.
go back to reference Schwartz, C.E., Sprangers, M.A.G., & Vollmer, T. (2010). A rashomon approach to detecting response shift in patients with multiple sclerosis: a head-to-head comparison of four statistical techniques. Quality of Life Research, Current Issue. Schwartz, C.E., Sprangers, M.A.G., & Vollmer, T. (2010). A rashomon approach to detecting response shift in patients with multiple sclerosis: a head-to-head comparison of four statistical techniques. Quality of Life Research, Current Issue.
7.
go back to reference Schwartz, C. E., Vollmer, T., & Lee, H. (1999). Reliability and validity of two self-report measures of impairment and disability for MS. Neurology, 52, 63–70.PubMed Schwartz, C. E., Vollmer, T., & Lee, H. (1999). Reliability and validity of two self-report measures of impairment and disability for MS. Neurology, 52, 63–70.PubMed
8.
go back to reference Ware, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey—Construction of scales and preliminary tests of reliability and validity. Medical Care, 34, 220–233.PubMedCrossRef Ware, J. E., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey—Construction of scales and preliminary tests of reliability and validity. Medical Care, 34, 220–233.PubMedCrossRef
9.
go back to reference Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data Via Em Algorithm. Journal of the Royal Statistical Society Series B-Methodological, 39, 1–38. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum Likelihood from Incomplete Data Via Em Algorithm. Journal of the Royal Statistical Society Series B-Methodological, 39, 1–38.
10.
go back to reference Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238–246.PubMedCrossRef Bentler, P. M. (1990). Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238–246.PubMedCrossRef
11.
go back to reference Tucker, L. R., & Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 37, 1–10.CrossRef Tucker, L. R., & Lewis, C. (1973). The reliability coefficient for maximum likelihood factor analysis. Psychometrika, 37, 1–10.CrossRef
12.
go back to reference Browne, M. W., & Cudeck, R. (1992). Alternative Ways of Assessing Model Fit. Sociological Methods & Research, 21, 230–258.CrossRef Browne, M. W., & Cudeck, R. (1992). Alternative Ways of Assessing Model Fit. Sociological Methods & Research, 21, 230–258.CrossRef
13.
go back to reference Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley. Bollen, K. A. (1989). Structural Equations with Latent Variables. New York: Wiley.
14.
go back to reference Tabachnick, B. G., & Fidel, L. S. (2006). Using Multivariate Statistics. Allyn & Bacon, Inc, Needham Heights. USA: MA. Tabachnick, B. G., & Fidel, L. S. (2006). Using Multivariate Statistics. Allyn & Bacon, Inc, Needham Heights. USA: MA.
15.
go back to reference Saris, W. E., Satorra, A., & van der Veld, W. M. (2009). Testing Structural Equation Models or Detection of Misspecifications? Structural Equation Modeling-A Multidisciplinary Journal, 16, 561–582.CrossRef Saris, W. E., Satorra, A., & van der Veld, W. M. (2009). Testing Structural Equation Models or Detection of Misspecifications? Structural Equation Modeling-A Multidisciplinary Journal, 16, 561–582.CrossRef
16.
go back to reference Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawerence Erlbaum associates. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawerence Erlbaum associates.
17.
go back to reference Neale, M. C. (2010). MxGui. Richmond, VA: In. VCU. Neale, M. C. (2010). MxGui. Richmond, VA: In. VCU.
18.
go back to reference Jöreskog, K. G., & Sörbom, D. L. I. S. R. E. L. (1996). 6 user’s guide (Vol. 2). Chicago, IL: Scientific Software International, Inc. Jöreskog, K. G., & Sörbom, D. L. I. S. R. E. L. (1996). 6 user’s guide (Vol. 2). Chicago, IL: Scientific Software International, Inc.
19.
go back to reference King-Kallimanis, B. L., Oort, F. J., & Garst, G. J. A. (2010). Using structural equation modelling to detect measurement bias and response shift in longitudinal data. Asta-Advances in Statistical Analysis, 94, 139–156.CrossRef King-Kallimanis, B. L., Oort, F. J., & Garst, G. J. A. (2010). Using structural equation modelling to detect measurement bias and response shift in longitudinal data. Asta-Advances in Statistical Analysis, 94, 139–156.CrossRef
20.
go back to reference Koch, M., Kingwell, E., Rieckmann, P., & Tremlett, H. (2009). The natural history of primary progressive multiple sclerosis. Neurology, 73, 1996–2002.PubMedCrossRef Koch, M., Kingwell, E., Rieckmann, P., & Tremlett, H. (2009). The natural history of primary progressive multiple sclerosis. Neurology, 73, 1996–2002.PubMedCrossRef
21.
go back to reference Sola, P., Mandrioli, J., Simone, A.M., Ferraro, D., Bedin, R., Annecca, R., et al. (2010). Primary progressive versus relapsing-onset multiple sclerosis: presence and prognostic value of cerebrospinal fluid oligoclonal IgM. Multiple Sclerosis, doi: 10.1177/1352458510386996. Sola, P., Mandrioli, J., Simone, A.M., Ferraro, D., Bedin, R., Annecca, R., et al. (2010). Primary progressive versus relapsing-onset multiple sclerosis: presence and prognostic value of cerebrospinal fluid oligoclonal IgM. Multiple Sclerosis, doi: 10.​1177/​1352458510386996​.
22.
go back to reference Oort, F. J. (2001). Three-mode models for multivariate longitudinal data. British Journal of Mathematical and Statistical Psychology, 54, 49–78.PubMedCrossRef Oort, F. J. (2001). Three-mode models for multivariate longitudinal data. British Journal of Mathematical and Statistical Psychology, 54, 49–78.PubMedCrossRef
Metadata
Title
Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients
Authors
Bellinda L. King-Kallimanis
Frans. J. Oort
Sandra Nolte
Carolyn E. Schwartz
Mirjam A. G. Sprangers
Publication date
01-12-2011
Publisher
Springer Netherlands
Published in
Quality of Life Research / Issue 10/2011
Print ISSN: 0962-9343
Electronic ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-010-9844-9

Other articles of this Issue 10/2011

Quality of Life Research 10/2011 Go to the issue