Analytical results in longitudinal studies depended on target of inference and assumed mechanism of attrition

J Clin Epidemiol. 2015 Oct;68(10):1165-75. doi: 10.1016/j.jclinepi.2015.03.011. Epub 2015 Mar 31.


Objectives: To compare methods for analysis of longitudinal studies with missing data due to participant dropout and follow-up truncated by death.

Study design and setting: We analyzed physical functioning in an Australian longitudinal study of elderly women where the missing data mechanism could either be missing at random (MAR) or missing not at random (MNAR). We assumed either an immortal cohort where deceased participants are implicitly included after death or a mortal cohort where the target of inference is surviving participants at each survey wave. To illustrate the methods a covariate was included. Simulation was used to assess the effect of the assumptions.

Results: Ignoring attrition or restricting analysis to participants with complete follow up led to biased estimates. Linear mixed model was appropriate for an immortal cohort under MAR but not MNAR. Linear increment model and joint modeling of longitudinal outcome and time to death were the most robust to MNAR. For a mortal cohort, inverse probability weighting and multiple imputation could be used, but care is needed in specifying dropout and imputation models, respectively.

Conclusion: Appropriate analysis methodology to deal with attrition in longitudinal studies depends on the target of inference and the missing data mechanism.

Keywords: Attrition; Dropout; Longitudinal study; Missing data; Mortal cohort; Simulation study.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Australia
  • Data Interpretation, Statistical*
  • Female
  • Humans
  • Linear Models
  • Longitudinal Studies*
  • Models, Statistical
  • Mortality
  • Patient Dropouts
  • Research Design