Asymptotic bias in the linear mixed effects model under non-ignorable missing data mechanisms

Chandan Saha, Michael P. Jones

Research output: Contribution to journalArticle

15 Scopus citations


In longitudinal studies, missingness of data is often an unavoidable problem. Estimators from the linear mixed effects model assume that missing data are missing at random. However, estimators are biased when this assumption is not met. In the paper, theoretical results for the asymptotic bias are established under non-ignorable drop-out, drop-in and other missing data patterns. The asymptotic bias is large when the drop-out subjects have only one or no observation, especially for slope-related parameters of the linear mixed effects model. In the drop-in case, intercept-related parameter estimators show substantial asymptotic bias when subjects enter late in the study. Eight other missing data patterns are considered and these produce asymptotic biases of a variety of magnitudes.

Original languageEnglish (US)
Pages (from-to)167-182
Number of pages16
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number1
StatePublished - Feb 8 2005


  • Clinical trial
  • Incomplete data
  • Informative drop-out
  • Longitudinal study
  • Repeated measurements

ASJC Scopus subject areas

  • Mathematics(all)
  • Statistics and Probability

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