Testing ignorable missingness in estimating equation approaches for longitudinal data

Annie Qu, Peter X.K. Song

Research output: Contribution to journalArticlepeer-review


We address the matter of determining whether or not missing data in longitudinal studies are ignorable with regard to quasilikelihood or estimating equations approaches. This involves testing for whether or not the zero-mean property of estimating equations holds true. Chen & Little (1999) proposed testing for significant differences among parameter estimators calculated from sample subsets with different patterns of missing data, whereas we propose a more unified generalised score-type test. This avoids exhaustive estimation of parameters for each missing-data pattern, testing instead with a single quadratic score test statistic whether or not there is a common parameter under which the means of all the pattern-specific estimating equations are zero. Comparisons are made for the two approaches with both simulations and real data examples.

Original languageEnglish (US)
Pages (from-to)841-850
Number of pages10
Issue number4
StatePublished - 2002


  • Generalised estimating equation
  • Goodness-of-fit test
  • Ignorable missingness
  • Quadratic inference function
  • Schizophrenia trial

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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