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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.

The relative efficiency of time‐to‐progression and continuous measures of cognition in presymptomatic Alzheimer's disease