  Pre-symptomatic (or Preclinical) Alzheimer's Disease is defined by biomarker
evidence of fibrillar amyloid beta pathology in the absence of clinical
symptoms. Clinical trials in this early phase of disease are challenging due to
the slow rate of disease progression as measured by periodic cognitive
performance tests or by transition to a diagnosis of Mild Cognitive Impairment.
In a multisite study, experts provide diagnoses by central chart review without
the benefit of in-person assessment. We use a simulation study to demonstrate
that models of repeated cognitive assessments detect treatment effects more
efficiently compared to models of time-to-progression to an endpoint such as
change in diagnosis. 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. We
find that power is approximately doubled with models of repeated continuous
outcomes compared to the time-to-progression analysis. The simulations also
demonstrate that a plausible informative missing data pattern can induce a bias
which inflates treatment effects, yet 5% Type I error is maintained.
