Iddi, S.Li, D.Aisen, P.S.Thompson, W.K.Donohue, M.C.2019-12-122019-12-122019-07-18https://doi.org/10.1016/j.trci.2019.04.004http://ugspace.ug.edu.gh/handle/123456789/34163Research ArticleIntroduction: Clinical trials on preclinical Alzheimer’s disease are challenging because of the slow rate of disease progression.We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently than models of time to progression. Methods: Multivariate continuous data are simulated from a Bayesian joint mixed-effects model fit to data from the Alzheimer’s Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data. Results: We find that power is approximately doubled with models of repeated continuous outcomes compared with the time-to-progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias that inflates treatment effects, yet 5% type I error is maintained. Discussion: Given the relative inefficiency of time to progression, it should be avoided as a primary analysis approach in clinical trials of preclinical Alzheimer’s disease.enClinical trial simulationsAlzheimer’s diseaseCox proportional hazards modelLongitudinal dataMixed model of repeated measures (MMRM)Article