TY - JOUR
T1 - Two-period linear mixed effects models to analyze clinical trials with run-in data when the primary outcome is continuous
T2 - Applications to Alzheimer's disease
AU - Wang, Guoqiao
AU - Aschenbrenner, Andrew J.
AU - Li, Yan
AU - McDade, Eric
AU - Liu, Lei
AU - Benzinger, Tammie L.S.
AU - Bateman, Randall J.
AU - Morris, John C.
AU - Hassenstab, Jason J.
AU - Xiong, Chengjie
N1 - Publisher Copyright:
© 2019 The Authors
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Alzheimer's disease
KW - Linear mixed effects model
KW - Run-in clinical trials
KW - Two-period models
KW - Unequal randomization
UR - http://www.scopus.com/inward/record.url?scp=85071670542&partnerID=8YFLogxK
U2 - 10.1016/j.trci.2019.07.007
DO - 10.1016/j.trci.2019.07.007
M3 - Article
C2 - 31517032
AN - SCOPUS:85071670542
SN - 2352-8737
VL - 5
SP - 450
EP - 457
JO - Alzheimer's and Dementia: Translational Research and Clinical Interventions
JF - Alzheimer's and Dementia: Translational Research and Clinical Interventions
ER -