Bias in the last observation carried forward method under informative dropout

Chandan Saha, Michael P. Jones

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


Subject dropout is an inevitable problem in longitudinal studies. It makes the analysis challenging when the main interest is the change in outcome from baseline to endpoint of study. The last observation carried forward (LOCF) method is a very common approach for handling this problem. It assumes that the last measured outcome is frozen in time after the point of dropout, an unrealistic assumption given any time trends. Though existence and direction of the bias can sometimes be anticipated, the more important statistical question involves the actual magnitude of the bias and this requires computation. This paper provides explicit expressions for the exact bias in the LOCF estimates of mean change and its variance when the longitudinal data follow a linear mixed-effects model with linear time trajectories. General dropout patterns are considered that may depend on treatment group, subject-specific trajectories and follow different time to dropout distributions. In our case studies, the magnitude of bias for mean change estimators linearly increases as time to dropout decreases. The bias depends heavily on the dropout interval. The variance term is always underestimated.

Original languageEnglish (US)
Pages (from-to)246-255
Number of pages10
JournalJournal of Statistical Planning and Inference
Issue number2
StatePublished - Feb 1 2009


  • Clinical trial
  • Incomplete data
  • Informative dropout
  • LOCF
  • Longitudinal study
  • Repeated measurements

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Statistics and Probability

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