caa a22 4500 606179275 CHVBK 20210128100800.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s11136-014-0824-3 doi (NATIONALLICENCE)springer-10.1007/s11136-014-0824-3 Identifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods [Elektronische Daten] [Tolulope Sajobi, Lisa Lix, Gurbakhshash Singh, Mark Lowerison, Jordan Engbers, Nancy Mayo] Purpose: Response shift (RS) is an important phenomenon that influences the assessment of longitudinal changes in health-related quality of life (HRQOL) studies. Given that RS effects are often small, missing data due to attrition or item non-response can contribute to failure to detect RS effects. Since missing data are often encountered in longitudinal HRQOL data, effective strategies to deal with missing data are important to consider. This study aims to compare different imputation methods on the detection of reprioritization RS in the HRQOL of caregivers of stroke survivors. Methods: Data were from a Canadian multi-center longitudinal study of caregivers of stroke survivors over a one-year period. The Stroke Impact Scale physical function score at baseline, with a cutoff of 75, was used to measure patient stroke severity for the reprioritization RS analysis. Mean imputation, likelihood-based expectation-maximization imputation, and multiple imputation methods were compared in test procedures based on changes in relative importance weights to detect RS in SF-36 domains over a 6-month period. Monte Carlo simulation methods were used to compare the statistical powers of relative importance test procedures for detecting RS in incomplete longitudinal data under different missing data mechanisms and imputation methods. Results: Of the 409 caregivers, 15.9 and 31.3% of them had missing data at baseline and 6months, respectively. There were no statistically significant changes in relative importance weights on any of the domains when complete-case analysis was adopted. But statistical significant changes were detected on physical functioning and/or vitality domains when mean imputation or EM imputation was adopted. There were also statistically significant changes in relative importance weights for physical functioning, mental health, and vitality domains when multiple imputation method was adopted. Our simulations revealed that relative importance test procedures were least powerful under complete-case analysis method and most powerful when a mean imputation or multiple imputation method was adopted for missing data, regardless of the missing data mechanism and proportion of missing data. Conclusions: Test procedures based on relative importance measures are sensitive to the type and amount of missing data and imputation method. Relative importance test procedures based on mean imputation and multiple imputation are recommended for detecting RS in incomplete data. Springer International Publishing Switzerland, 2014 Missing data nationallicence Response shift nationallicence Relative importance analysis nationallicence Health-related quality of life nationallicence Sajobi Tolulope Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, T2N 4Z6, Calgary, Canada aut Lix Lisa Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada aut Singh Gurbakhshash Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, T2N 4Z6, Calgary, Canada aut Lowerison Mark Clinical Research Unit, University of Calgary, Calgary, Canada aut Engbers Jordan Clinical Research Unit, University of Calgary, Calgary, Canada aut Mayo Nancy Division of Clinical Epidemiology, McGill University Health Center Research Institute, Montreal, Canada aut Quality of Life Research Springer International Publishing 24/3(2015-03-01), 529-540 0962-9343 24:3<529 2015 24 11136 https://doi.org/10.1007/s11136-014-0824-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11136-014-0824-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sajobi Tolulope Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, T2N 4Z6, Calgary, Canada aut NATIONALLICENCE 700 1- Lix Lisa Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada aut NATIONALLICENCE 700 1- Singh Gurbakhshash Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, T2N 4Z6, Calgary, Canada aut NATIONALLICENCE 700 1- Lowerison Mark Clinical Research Unit, University of Calgary, Calgary, Canada aut NATIONALLICENCE 700 1- Engbers Jordan Clinical Research Unit, University of Calgary, Calgary, Canada aut NATIONALLICENCE 700 1- Mayo Nancy Division of Clinical Epidemiology, McGill University Health Center Research Institute, Montreal, Canada aut NATIONALLICENCE 773 0- Quality of Life Research Springer International Publishing 24/3(2015-03-01), 529-540 0962-9343 24:3<529 2015 24 11136