Bflcrm: A bayesian functional linear cox regression model for predicting time to conversion to alzheimer’s disease

Alzheimer’s disease neuroimaging initiative

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer’s disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer’s Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog) and APOE-ε4 status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.

Original languageEnglish (US)
Pages (from-to)2153-2178
Number of pages26
JournalAnnals of Applied Statistics
Volume9
Issue number4
DOIs
StatePublished - Dec 1 2015

Keywords

  • Alzheimer’s disease
  • Functional principal component analysis
  • Hippocampus surface morphology
  • Mild cognitive impairment
  • Proportional hazard model

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability

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