title: Identifying reprioritization response shift in a stroke caregiver population: a comparison of missing data methods.
abstract: OBJECTIVE
  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 6 months, 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.
authors.affiliation: Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, T2N 4Z6, Canada, tolu.sajobi@ucalgary.ca.
authors.forename: Tolulope T
authors.initials: TT
authors.lastname: Sajobi
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Lix
authors.forename: Gurbakhshash
authors.initials: G
authors.lastname: Singh
authors.forename: Mark
authors.initials: M
authors.lastname: Lowerison
authors.forename: Jordan
authors.initials: J
authors.lastname: Engbers
authors.forename: Nancy E
authors.initials: NE
authors.lastname: Mayo
mesh_terms: D002170:Canada; D017028:Caregivers / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D011788:Quality of Life / Q000523:psychology*; D012107:Research Design / Q000706:statistics & numerical data*; D020521:Stroke; D017741:Survivors