title: Minimizing attrition bias: a longitudinal study of depressive symptoms in an elderly cohort.
abstract: BACKGROUND
  Attrition from mortality is common in longitudinal studies of the elderly. Ignoring the resulting non-response or missing data can bias study results.
  
  
  METHODS
  1260 elderly participants underwent biennial follow-up assessments over 10 years. Many missed one or more assessments over this period. We compared three statistical models to evaluate the impact of missing data on an analysis of depressive symptoms over time. The first analytic model (generalized mixed model) treated non-response as data missing at random. The other two models used shared parameter methods; each had different specifications for dropout but both jointly modeled both outcome and dropout through a common random effect.
  
  
  RESULTS
  The presence of depressive symptoms was associated with being female, having less education, functional impairment, using more prescription drugs, and taking antidepressant drugs. In all three models, the same variables were significantly associated with depression and in the same direction. However, the strength of the associations differed widely between the generalized mixed model and the shared parameter models. Although the two shared parameter models had different assumptions about the dropout process, they yielded similar estimates for the outcome. One model fitted the data better, and the other was computationally faster.
  
  
  CONCLUSIONS
  Dropout does not occur randomly in longitudinal studies of the elderly. Thus, simply ignoring it can yield biased results. Shared parameter models are a powerful, flexible, and easily implemented tool for analyzing longitudinal data while minimizing bias due to nonrandom attrition.
authors.affiliation: Department of Medicine, University of Pittsburgh, PA 15213, USA. changj@pitt.edu
authors.forename: Chung-Chou H
authors.initials: CC
authors.lastname: Chang
authors.forename: Hsiao-Ching
authors.initials: HC
authors.lastname: Yang
authors.forename: Gong
authors.initials: G
authors.lastname: Tang
authors.forename: Mary
authors.initials: M
authors.lastname: Ganguli
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D015982:Bias; D015331:Cohort Studies; D003866:Depressive Disorder / Q000175:diagnosis / Q000453:epidemiology* / Q000401:mortality / Q000523:psychology; D005260:Female; D006306:Health Surveys; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D010555:Personality Inventory / Q000706:statistics & numerical data; D011594:Psychometrics; D012307:Risk Factors; D012424:Rural Population / Q000706:statistics & numerical data; D014481:United States