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.
Original language | English |
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Pages (from-to) | 308-318 |
Number of pages | 11 |
Journal | Alzheimer's and Dementia: Translational Research and Clinical Interventions |
Volume | 5 |
DOIs | |
Publication status | Published - 2019 |
Keywords
- Alzheimer's disease
- Bayesian joint mixed-effect model
- Clinical trial simulations
- Common close design
- Cox proportional hazards model
- Longitudinal data
- Mixed model of repeated measures (MMRM)
- Statistical power