Abstract:
Introduction: 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.
