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

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

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 languageEnglish
Pages (from-to)308-318
Number of pages11
JournalAlzheimer's and Dementia: Translational Research and Clinical Interventions
Volume5
DOIs
Publication statusPublished - 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

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