The relative efficiency of time-to-progression and continuous measures of cognition in pre-symptomatic Alzheimer's

Abstract

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.