Problematic formulations of SAS PROC.MIXED models for repeated measurements

J. E. Overall, C. Ahn, C. Shivakumar, Y. Kalburgi

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


The work reported in this article was undertaken to evaluate the utility of SAS PROC.MIXED for testing hypotheses concerning GROUP and TIME x GROUP effects in repeated measurements designs with dropouts. If dropouts are not completely at random, covariate control over informative individual differences on which dropout data patterns depend is widely recognized to be important. However, the inclusion of baseline scores and time-in-study as between-subject covariates in an otherwise well formulated SAS PROC.MIXED model resulted in inadequate control over type I error in simulated data with or without dropouts present. The inadequate model formulations and resulting deviant test sizes are presented here as a warning for others who might be guided by the same information sources to employ similar model specifications when analyzing data from actual clinical trials. It is important that the complete model specification be provided in detail when reporting applications of the general linear mixed-model procedure. A single random- coefficients model produced appropriate test sizes, hut it provided inferior power when informative covariates were added in the attempt to adjust for dropouts. As an alternative, the incorporation of covariate controls in simpler two-stage endpoint or random regression analyses is documented to be effective in dealing with dropouts under specifiable conditions.

Original languageEnglish (US)
Pages (from-to)189-216
Number of pages28
JournalJournal of Biopharmaceutical Statistics
Issue number1
StatePublished - 1999


  • Covariates
  • Missing data
  • Mixed models
  • Regression on time
  • Repeated measures
  • Test size

ASJC Scopus subject areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)


Dive into the research topics of 'Problematic formulations of SAS PROC.MIXED models for repeated measurements'. Together they form a unique fingerprint.

Cite this