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Gepubliceerd in: Quality of Life Research 8/2015

01-08-2015

Overall performance of Oort’s procedure for response shift detection at item level: a pilot simulation study

Auteurs: Antoine Vanier, Véronique Sébille, Myriam Blanchin, Alice Guilleux, Jean-Benoit Hardouin

Gepubliceerd in: Quality of Life Research | Uitgave 8/2015

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Abstract

Objective

This simulation study was designed to provide data on the performance of Oort’s procedure (OP) for response shift (RS) detection (regarding type I error, power, and overall performance), according to sample characteristics, at item level. A specific objective was to assess the impact of using different information criteria (IC), as alternatives to the LRT (likelihood-ratio test), for global assessment of RS occurrence.

Methods

Responses to five binary items at two times of measurement were simulated. Thirty-six combinations of sample characteristics [sample size (n), “true change,” correlations between the two latent variables and presence/absence of uniform recalibration RS (ur)] were considered. A thousand datasets were generated for each combination. RS detection was performed on each dataset following OP. Type I error and power of the global assessment of RS occurrence, as well as overall performance of the OP, were assessed.

Results

The estimated type I error was close to 5 % for the LRT and lower than 5 % for the IC. The estimated power was higher for the LRT as compared to the AIC, which was the highest among the other IC. For the LRT, the estimated power for n = 100 and for the combination of n = 200 and ur = 1 item was below 80 %. Otherwise, for other combinations of sample characteristics, the estimated power was above 90 %.

Conclusion

For the LRT, higher values of power were estimated compared to IC with appropriate values of type I error. These results were consistent with Oort’s proposal to use the LRT as the criterion to assess global RS occurrence.
Literatuur
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(11), 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(11), 1507–1515.PubMedCrossRef
2.
go back to reference Schwartz, C. E., Bode, R., Repucci, N., Becker, J., Sprangers, M. A. G., & Fayers, P. M. (2006). The clinical significance of adaptation to changing health: A meta-analysis of response shift. Quality of Life Research, 15(9), 1533–1550.PubMedCrossRef Schwartz, C. E., Bode, R., Repucci, N., Becker, J., Sprangers, M. A. G., & Fayers, P. M. (2006). The clinical significance of adaptation to changing health: A meta-analysis of response shift. Quality of Life Research, 15(9), 1533–1550.PubMedCrossRef
3.
go back to reference The SAMSI Psychometric Program Longitudinal Assessment of Patient-Reported Outcomes Working Group, Swartz, R. J., Schwartz, C., Basch, E., Cai, D. L., Fairclough, B., & Rapkin, L. (2011). The king’s foot of patient-reported outcomes: Current practices and new developments for the measurement of change. Quality of Life Research, 20(8), 1159–1167.PubMedCentralCrossRef The SAMSI Psychometric Program Longitudinal Assessment of Patient-Reported Outcomes Working Group, Swartz, R. J., Schwartz, C., Basch, E., Cai, D. L., Fairclough, B., & Rapkin, L. (2011). The king’s foot of patient-reported outcomes: Current practices and new developments for the measurement of change. Quality of Life Research, 20(8), 1159–1167.PubMedCentralCrossRef
4.
go back to reference Schwartz, C. E., & Sprangers, M. A. G. (1999). Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research. Social Science and Medicine, 48(11), 1531–1548.PubMedCrossRef Schwartz, C. E., & Sprangers, M. A. G. (1999). Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research. Social Science and Medicine, 48(11), 1531–1548.PubMedCrossRef
5.
go back to reference Barclay-Goddard, R., Epstein, J. D., & Mayo, N. E. (2009). Response shift: A brief overview and proposed research priorities. Quality of Life Research, 18(3), 335–346.PubMedCrossRef Barclay-Goddard, R., Epstein, J. D., & Mayo, N. E. (2009). Response shift: A brief overview and proposed research priorities. Quality of Life Research, 18(3), 335–346.PubMedCrossRef
6.
go back to reference Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14(3), 587–598.PubMedCrossRef Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14(3), 587–598.PubMedCrossRef
7.
go back to reference Raykov, T. (2006). A first course in structural equation modeling (2nd ed.). Mahwah: Lawrence Erlbaum Associates, Publishers. Raykov, T. (2006). A first course in structural equation modeling (2nd ed.). Mahwah: Lawrence Erlbaum Associates, Publishers.
8.
go back to reference Oort, F. J., Visser, M. R. M., & Sprangers, M. A. G. (2005). An application of structural equation modeling to detect response shifts and true change in quality of life data from cancer patients undergoing invasive surgery. Quality of Life Research, 14(3), 599–609.PubMedCrossRef Oort, F. J., Visser, M. R. M., & Sprangers, M. A. G. (2005). An application of structural equation modeling to detect response shifts and true change in quality of life data from cancer patients undergoing invasive surgery. Quality of Life Research, 14(3), 599–609.PubMedCrossRef
9.
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(3), 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(3), 629–639.PubMedCrossRef
10.
go back to reference Barclay-Goddard, R., Lix, L. M., Tate, R., Weinberg, L., & Mayo, N. E. (2011). Health-related quality of life after stroke: Does response shift occur in self-perceived physical function? Archives of Physical Medicine and Rehabilitation, 92(11), 1762–1769.PubMedCrossRef Barclay-Goddard, R., Lix, L. M., Tate, R., Weinberg, L., & Mayo, N. E. (2011). Health-related quality of life after stroke: Does response shift occur in self-perceived physical function? Archives of Physical Medicine and Rehabilitation, 92(11), 1762–1769.PubMedCrossRef
11.
go back to reference King-Kallimanis, B. L., Oort, F. J., Nolte, S., Schwartz, C. E., & Sprangers, M. A. G. (2011). Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients. Quality of Life Research, 20(10), 1527–1540.PubMedCentralPubMedCrossRef King-Kallimanis, B. L., Oort, F. J., Nolte, S., Schwartz, C. E., & Sprangers, M. A. G. (2011). Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients. Quality of Life Research, 20(10), 1527–1540.PubMedCentralPubMedCrossRef
12.
go back to reference Nagl, M., & Farin, E. (2012). Response shift in quality of life assessment in patients with chronic back pain and chronic ischaemic heart disease. Disability and Rehabilitation, 34(8), 671–680.PubMedCrossRef Nagl, M., & Farin, E. (2012). Response shift in quality of life assessment in patients with chronic back pain and chronic ischaemic heart disease. Disability and Rehabilitation, 34(8), 671–680.PubMedCrossRef
13.
go back to reference Fokkema, M., Smits, N., Kelderman, H., & Cuijpers, P. (2013). Response shifts in mental health interventions: An illustration of longitudinal measurement invariance. Psychological Assessment, 25(2), 520–531.PubMedCrossRef Fokkema, M., Smits, N., Kelderman, H., & Cuijpers, P. (2013). Response shifts in mental health interventions: An illustration of longitudinal measurement invariance. Psychological Assessment, 25(2), 520–531.PubMedCrossRef
14.
go back to reference Gandhi, P. K., Ried, L. D., Huang, I.-C., Kimberlin, C. L., & Kauf, T. L. (2013). Assessment of response shift using two structural equation modeling techniques. Quality of Life Research, 22(3), 461–471.PubMedCentralPubMedCrossRef Gandhi, P. K., Ried, L. D., Huang, I.-C., Kimberlin, C. L., & Kauf, T. L. (2013). Assessment of response shift using two structural equation modeling techniques. Quality of Life Research, 22(3), 461–471.PubMedCentralPubMedCrossRef
15.
go back to reference Barendse, M. T., Oort, F. J., Werner, C. S., Ligtvoet, R., & Schermelleh-Engel, K. (2012). Measurement bias detection through factor analysis. Structural Equation Modeling A Multidisciplinary Journal, 19(4), 561–579.CrossRef Barendse, M. T., Oort, F. J., Werner, C. S., Ligtvoet, R., & Schermelleh-Engel, K. (2012). Measurement bias detection through factor analysis. Structural Equation Modeling A Multidisciplinary Journal, 19(4), 561–579.CrossRef
16.
go back to reference Barendse, M. T., Oort, F. J., & Garst, G. J. A. (2010). Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study. AStA Advances in Statistical Analysis, 94(2), 117–127.CrossRef Barendse, M. T., Oort, F. J., & Garst, G. J. A. (2010). Using restricted factor analysis with latent moderated structures to detect uniform and nonuniform measurement bias; a simulation study. AStA Advances in Statistical Analysis, 94(2), 117–127.CrossRef
17.
go back to reference Woods, C. M., & Grimm, K. J. (2011). Testing for nonuniform differential item functioning with multiple indicator multiple cause models. Applied Psychological Measurement, 35(5), 339–361.CrossRef Woods, C. M., & Grimm, K. J. (2011). Testing for nonuniform differential item functioning with multiple indicator multiple cause models. Applied Psychological Measurement, 35(5), 339–361.CrossRef
18.
go back to reference Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.CrossRef Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.CrossRef
19.
go back to reference Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.CrossRef Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.CrossRef
20.
go back to reference Sclove, L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343.CrossRef Sclove, L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343.CrossRef
21.
go back to reference Fischer, G., & Molenaar, I. (1995). Rasch models: Foundation, recent developments, and applications. New-York: Springer.CrossRef Fischer, G., & Molenaar, I. (1995). Rasch models: Foundation, recent developments, and applications. New-York: Springer.CrossRef
22.
go back to reference Sébille, V., Hardouin, J.-B., Le Neel, T., Kubis, G., Boyer, F., Guillemin, F., & Falissard, B. (2010). Methodological issues regarding power of classical test theory and IRT-based approaches for the comparison of patient-reported outcome measures-a simulation study. BMC Medical Research Methodology, 10–24. Sébille, V., Hardouin, J.-B., Le Neel, T., Kubis, G., Boyer, F., Guillemin, F., & Falissard, B. (2010). Methodological issues regarding power of classical test theory and IRT-based approaches for the comparison of patient-reported outcome measures-a simulation study. BMC Medical Research Methodology, 10–24.
23.
go back to reference Satorra, A., & Bentler, P. (1994). Corrections to test statistics and standards errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousand Oaks: Sage. Satorra, A., & Bentler, P. (1994). Corrections to test statistics and standards errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousand Oaks: Sage.
24.
go back to reference Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
25.
go back to reference R Development Core Team. (2013). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. R Development Core Team. (2013). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.
26.
go back to reference Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness of fit measures. Methods of Psychological Research Online, 8(2), 23–74. Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness of fit measures. Methods of Psychological Research Online, 8(2), 23–74.
27.
go back to reference Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press. Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.
28.
go back to reference Nolte, S., Elsworth, G. R., Sinclair, A. J., & Osborne, R. H. (2009). Tests of measurement invariance failed to support the application of the “then-test”. Journal of Clinical Epidemiology, 62(11), 1173–1180.PubMedCrossRef Nolte, S., Elsworth, G. R., Sinclair, A. J., & Osborne, R. H. (2009). Tests of measurement invariance failed to support the application of the “then-test”. Journal of Clinical Epidemiology, 62(11), 1173–1180.PubMedCrossRef
29.
go back to reference Ahmed, S., Bourbeau, J., Maltais, F., & Mansour, A. (2009). The Oort structural equation modeling approach detected a response shift after a COPD self-management program not detected by the Schmitt technique. Journal of Clinical Epidemiology, 62(11), 1165–1172.PubMedCrossRef Ahmed, S., Bourbeau, J., Maltais, F., & Mansour, A. (2009). The Oort structural equation modeling approach detected a response shift after a COPD self-management program not detected by the Schmitt technique. Journal of Clinical Epidemiology, 62(11), 1165–1172.PubMedCrossRef
30.
go back to reference Bryant, F. B., & Satorra, A. (2012). Principles and practice of scaled difference chi square testing. Structural Equation Modeling A Multidisciplinary Journal, 19(3), 372–398.CrossRef Bryant, F. B., & Satorra, A. (2012). Principles and practice of scaled difference chi square testing. Structural Equation Modeling A Multidisciplinary Journal, 19(3), 372–398.CrossRef
31.
go back to reference Lehmann, E. L. (2008). Testing statistical hypotheses (3rd ed.). New York: Springer. Lehmann, E. L. (2008). Testing statistical hypotheses (3rd ed.). New York: Springer.
32.
go back to reference Hu, L., & Bentler, P. (1995). Evaluating model fit. In Structural equation modeling. Concepts, issues, and applications (p. 76–99). London: Sage. Hu, L., & Bentler, P. (1995). Evaluating model fit. In Structural equation modeling. Concepts, issues, and applications (p. 76–99). London: Sage.
33.
go back to reference Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework. Medical Care, 44(11 Suppl 3), S78.PubMedCentralPubMedCrossRef Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups? Testing measurement invariance using the confirmatory factor analysis framework. Medical Care, 44(11 Suppl 3), S78.PubMedCentralPubMedCrossRef
34.
go back to reference Finney, S. J., & DiStefano, C. (2013). Non-normal and categorical data in structural equation modeling. In Structural Equation (Ed.), Modeling: A second course (pp. 439–492). Charlotte: IAP, Information Age Publ. Finney, S. J., & DiStefano, C. (2013). Non-normal and categorical data in structural equation modeling. In Structural Equation (Ed.), Modeling: A second course (pp. 439–492). Charlotte: IAP, Information Age Publ.
35.
go back to reference Beaujean, A. (2014). Models with dichotomous indicator variables. In Latent variable modeling using R. A step-by-step guide (p. 93–113). New-York, NY: Taylor and Francis. Beaujean, A. (2014). Models with dichotomous indicator variables. In Latent variable modeling using R. A step-by-step guide (p. 93–113). New-York, NY: Taylor and Francis.
36.
go back to reference Barclay-Goddard, R., Lix, L. M., Tate, R., Weinberg, L., & Mayo, N. E. (2009). Response shift was identified over multiple occasions with a structural equation modeling framework. Journal of Clinical Epidemiology, 62(11), 1181–1188.PubMedCrossRef Barclay-Goddard, R., Lix, L. M., Tate, R., Weinberg, L., & Mayo, N. E. (2009). Response shift was identified over multiple occasions with a structural equation modeling framework. Journal of Clinical Epidemiology, 62(11), 1181–1188.PubMedCrossRef
Metagegevens
Titel
Overall performance of Oort’s procedure for response shift detection at item level: a pilot simulation study
Auteurs
Antoine Vanier
Véronique Sébille
Myriam Blanchin
Alice Guilleux
Jean-Benoit Hardouin
Publicatiedatum
01-08-2015
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 8/2015
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-015-0938-2

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