title: Method variation in the impact of missing data on response shift detection.
abstract: OBJECTIVE
  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.
authors.affiliation: DeltaQuest Foundation, Inc, Concord, MA, USA, carolyn.schwartz@deltaquest.org.
authors.forename: Carolyn E
authors.initials: CE
authors.lastname: Schwartz
authors.forename: Tolulope T
authors.initials: TT
authors.lastname: Sajobi
authors.forename: Mathilde G E
authors.initials: MG
authors.lastname: Verdam
authors.forename: Veronique
authors.initials: V
authors.lastname: Sebille
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Lix
authors.forename: Alice
authors.initials: A
authors.lastname: Guilleux
authors.forename: Mirjam A G
authors.initials: MA
authors.lastname: Sprangers
mesh_terms: D000368:Aged; D000465:Algorithms; D017028:Caregivers; D002908:Chronic Disease / Q000523:psychology*; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009369:Neoplasms / Q000523:psychology; D011788:Quality of Life / Q000523:psychology*; D012107:Research Design / Q000706:statistics & numerical data*; D057566:Self Report; D020521:Stroke / Q000523:psychology