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Some IRT models have the advantage of being robust to missing data and thus can be used with complete data as well as different patterns of missing data (informative or not). The purpose of this paper was to develop an algorithm for response shift (RS) detection using IRT models allowing for non-uniform and uniform recalibration, reprioritization RS recognition and true change estimation with these forms of RS taken into consideration if appropriate.
The algorithm is described, and its implementation is shown and compared to Oort’s structural equation modeling (SEM) procedure using data from a clinical study assessing health-related quality of life in 669 hospitalized patients with chronic conditions.
The results were quite different for the two methods. Both showed that some items of the SF-36 General Health subscale were affected by response shift, but those items usually differed between IRT and SEM. The IRT algorithm found evidence of small recalibration and reprioritization effects, whereas SEM mostly found evidence of small recalibration effects.
An algorithm has been developed for response shift analyses using IRT models and allows the investigation of non-uniform and uniform recalibration as well as reprioritization. Differences in RS detection between IRT and SEM may be due to differences between the two methods in handling missing data. However, one cannot conclude on the differences between IRT and SEM based on a single application on a dataset since the underlying truth is unknown. A next step would be to implement a simulation study to investigate those differences.
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Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 14(3), 587–598. CrossRef
Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. Hoboken: Wiley. CrossRef
Fischer, G. H., & Molenaar, I. W. (1995). Rasch models: Foundations, recent developments, and applications. New York: Springer. CrossRef
Wang, W.-C., & Chyi-In, W. (2004). Gain score in item response theory as an effect size measure. Educational and Psychological Measurement, 64(5), 758–780. CrossRef
Andrich, D. (2011). Rating scales and rasch measurement. Expert Review of Pharmacoeconomics & Outcomes Research, 11, 571–585. CrossRef
De Bock, E., Hardouin, J. -B., Blanchin, M., Le Neel, T., Kubis, G., Bonnaud-Antignac, A., et al. (2013). Rasch-family models are more valuable than score-based approaches for analysing longitudinal patient-reported outcomes with missing data. Statistical Methods in Medical Research (in press).
De Bock, É. de, Hardouin, J. -B., Blanchin, M., Neel, T. L., Kubis, G., & Sébille, V. (2014). Assessment of score- and Rasch-based methods for group comparison of longitudinal patient-reported outcomes with intermittent missing data (informative and non-informative). Quality of Life Research, 1–11.
Hamel J. F., Sébille V., Le Neel T., Kubis G., & Hardouin J. B. (2012) Study of different methods for comparing groups by analysis of patients reported outcomes: Item response theory based methods seem more efficient than classical test theory based methods when data is missing. Under review.
Schwartz, C. E., & Sprangers, M. A. (1999). Methodological approaches for assessing response shift in longitudinal health-related quality-of-life research. Social Science & Medicine, 48(11), 1531–1548.
Glas, C. A. W. (1988). The derivation of some tests for the Rasch model from the multinomial distribution. Psychometrika, 53, 525–546. CrossRef
Glas, C. A. W. (2010). http://www.utwente.nl/gw/omd/Medewerkers/temp_test/mirt-manual.pdf
Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousand Oaks, CA, US: Sage Publications, Inc.
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
R Development Core Team. (n.d.). R Development Core Team. (2013). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
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, 23–74.
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
Enders, C. K. (2013). Analyzing structural equation models with missing data. In Structural Equation (Ed.), Modeling : a second course (pp. 493–519). Charlotte, NC: IAP, Information Age Publ.
Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8(3), 430–457. CrossRef
Kepka, S., Baumann, C., Anota, A., Buron, G., Spitz, E., Auquier, P., Guillemin, F., Mercier, M. (2013). The relationship between traits optimism and anxiety and health-related quality of life in patients hospitalized for chronic diseases: data from the SATISQOL study. Health and Quality of Life Outcomes, 11(1), 134.
Leplège, A. (2001). Le questionnaire MOS SF-36: manuel de l’utilisateur et guide d’interprétation des scores. Paris: Editions ESTEM.
Fairclough, D. L. (2002). Design and analysis of quality of life studies in clinical trials: Interdisciplinary statistics. London: Chapman & Hall/Crc.
- RespOnse Shift ALgorithm in Item response theory (ROSALI) for response shift detection with missing data in longitudinal patient-reported outcome studies
Carolyn E. Schwartz
- Springer International Publishing