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December 2015 BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease
Eunjee Lee, Hongtu Zhu, Dehan Kong, Yalin Wang, Kelly Sullivan Giovanello, Joseph G. Ibrahim, for the Alzheimer's Disease Neuroimaging Initiative
Ann. Appl. Stat. 9(4): 2153-2178 (December 2015). DOI: 10.1214/15-AOAS879

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-$\varepsilon4$ 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.

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Eunjee Lee. Hongtu Zhu. Dehan Kong. Yalin Wang. Kelly Sullivan Giovanello. Joseph G. Ibrahim. for the Alzheimer's Disease Neuroimaging Initiative. "BFLCRM: A Bayesian functional linear Cox regression model for predicting time to conversion to Alzheimer’s disease." Ann. Appl. Stat. 9 (4) 2153 - 2178, December 2015. https://doi.org/10.1214/15-AOAS879

Information

Received: 1 July 2014; Revised: 1 August 2015; Published: December 2015
First available in Project Euclid: 28 January 2016

zbMATH: 06560826
MathSciNet: MR3456370
Digital Object Identifier: 10.1214/15-AOAS879

Rights: Copyright © 2015 Institute of Mathematical Statistics

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Vol.9 • No. 4 • December 2015
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