Format

Send to

Choose Destination
Alzheimers Dement (N Y). 2019 Sep 5;5:450-457. doi: 10.1016/j.trci.2019.07.007. eCollection 2019.

Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous: Applications to Alzheimer's disease.

Author information

1
Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
2
Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA.
3
Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
4
Department of Psychological and Brain Sciences, Washington University School of Medicine, St. Louis, MO, USA.

Abstract

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.

KEYWORDS:

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

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center