Introduction: Study outcomes can be measured repeatedly based on the clinical trial protocol before randomization during what is known as the “run-in” period. However, it has not been established how best to incorporate run-in data into the primary analysis of the trial. Methods: We proposed two-period (run-in period and randomization period) linear mixed effects models to simultaneously model the run-in data and the postrandomization data. Results: Compared with the traditional models, the two-period linear mixed effects models can increase the power up to 15% and yield similar power for both unequal randomization and equal randomization. Discussion: Given that analysis of run-in data using the two-period linear mixed effects models allows more participants (unequal randomization) to be on the active treatment with similar power to that of the equal-randomization trials, it may reduce the dropout by assigning more participants to the active treatment and thus improve the efficiency of AD clinical trials.

Original languageEnglish
Pages (from-to)450-457
Number of pages8
JournalAlzheimer's and Dementia: Translational Research and Clinical Interventions
StatePublished - 2019


  • Alzheimer's disease
  • Linear mixed effects model
  • Run-in clinical trials
  • Two-period models
  • Unequal randomization


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