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Schwartz CE, Sajobi TT, Verdam MG, et al. Method variation in the impact of missing data on response shift detection. Qual Life Res. 2014;24(3):521-8doi: 10.1007/s11136-014-0746-0.
Schwartz, C. E., Sajobi, T. T., Verdam, M. G., Sebille, V., Lix, L. M., Guilleux, A., & Sprangers, M. A. (2015). Method variation in the impact of missing data on response shift detection. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation, 24(3), 521-8. https://doi.org/10.1007/s11136-014-0746-0
Schwartz, Carolyn E, et al. "Method variation in the impact of missing data on response shift detection." Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation vol. 24,3 (2015): 521-8. doi: https://doi.org/10.1007/s11136-014-0746-0
Schwartz CE, Sajobi TT, Verdam MG, Sebille V, Lix LM, Guilleux A, Sprangers MA. Method variation in the impact of missing data on response shift detection. Qual Life Res. 2015 Mar;24(3):521-8. doi: 10.1007/s11136-014-0746-0. Epub 2014 Jul 10. PMID: 25008260.
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Qual Life Res. 2015 Mar;24(3):521-8. doi: 10.1007/s11136-014-0746-0. Epub 2014 Jul 10.

Method variation in the impact of missing data on response shift detection.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation

Carolyn E Schwartz, Tolulope T Sajobi, Mathilde G E Verdam, Veronique Sebille, Lisa M Lix, Alice Guilleux, Mirjam A G Sprangers

Affiliations

  1. DeltaQuest Foundation, Inc, Concord, MA, USA, [email protected].

PMID: 25008260 DOI: 10.1007/s11136-014-0746-0

Abstract

PURPOSE: Missing data due to attrition or item non-response can result in biased estimates and loss of power in longitudinal quality-of-life (QOL) research. The impact of missing data on response shift (RS) detection is relatively unknown. This overview article synthesizes the findings of three methods tested in this special section regarding the impact of missing data patterns on RS detection in incomplete longitudinal data.

METHODS: The RS detection methods investigated include: (1) Relative importance analysis to detect reprioritization RS in stroke caregivers; (2) Oort's structural equation modeling (SEM) to detect recalibration, reprioritization, and reconceptualization RS in cancer patients; and (3) Rasch-based item-response theory-based (IRT) models as compared to SEM models to detect recalibration and reprioritization RS in hospitalized chronic disease patients. Each method dealt with missing data differently, either with imputation (1), attrition-based multi-group analysis (2), or probabilistic analysis that is robust to missingness due to the specific objectivity property (3).

RESULTS: Relative importance analyses were sensitive to the type and amount of missing data and imputation method, with multiple imputation showing the largest RS effects. The attrition-based multi-group SEM revealed differential effects of both the changes in health-related QOL and the occurrence of response shift by attrition stratum, and enabled a more complete interpretation of findings. The IRT RS algorithm found evidence of small recalibration and reprioritization effects in General Health, whereas SEM mostly evidenced small recalibration effects. These differences may be due to differences between the two methods in handling of missing data.

CONCLUSIONS: Missing data imputation techniques result in different conclusions about the presence of reprioritization RS using the relative importance method, while the attrition-based SEM approach highlighted different recalibration and reprioritization RS effects by attrition group. The IRT analyses detected more recalibration and reprioritization RS effects than SEM, presumably due to IRT's robustness to missing data. Future research should apply simulation techniques in order to make conclusive statements about the impacts of missing data according to the type and amount of RS.

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