On the impact of parametric assumptions and robust alternatives for longitudinal data analysis

Naiji Lu*, Wan Tang, Hua He, Qin Yu, Paul Crits-Christoph, Hui Zhang, Xin Tu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Models for longitudinal data are employed in a wide range of behavioral, biomedical, psychosocial, and health-care-related research. One popular model for continuous response is the linear mixedeffects model (LMM). Although simulations by recent studies show that LMM provides reliable estimates under departures from the normality assumption for complete data, the invariable occurrence of missing data in practical studies renders such robustness results less useful when applied to real study data. In this paper, we show by simulated studies that in the presence of missing data estimates of the fixed effect of LMM are biased under departures from normality. We discuss two robust alternatives, the weighted generalized estimating equations (WGEE) and the augmented WGEE (AWGEE), and compare their performances with LMM using real as well as simulated data. Our simulation results show that both WGEE and AWGEE provide valid inference for skewed nonnormal data when missing data follows the missing at random, the most popular missing data mechanism for real study data.

Original languageEnglish (US)
Pages (from-to)627-643
Number of pages17
JournalBiometrical Journal
Issue number4
StatePublished - Aug 2009


  • Augmented weighted generalized estimating equations
  • Double robust estimate
  • Missing at random
  • Surrogacy assumption
  • Weighted generalized estimating equations

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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