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BFLCRM: A Bayesian Functional Linear Cox Regression Model for Predicting Time to Conversion to Alzheimer’s Disease

1Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
2School of Computing, Informatics, and Decision Systems Engineering Arizona State University Tempe, AZ, USA.
Publication Date:
Ann Appl Stat
Volume Number:
Issue Number:
Ann Appl Stat. 2015 Dec;9(4):2153-2178.
PubMed ID:
Alzheimer’s disease, functional principal component analysis, hippocampus surface morphology, mild cognitive impairment, proportional hazard model
Appears in Collections:
UL1 TR001111/TR/NCATS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R01 MH086633/MH/NIMH NIH HHS/United States
R01 GM070335/GM/NIGMS NIH HHS/United States
UL1 RR025747/RR/NCRR NIH HHS/United States
T32 CA106209/CA/NCI NIH HHS/United States
P01 CA142538/CA/NCI NIH HHS/United States
R21 AG033387/AG/NIA NIH HHS/United States
TL1 TR001110/TR/NCATS NIH HHS/United States
R01 CA074015/CA/NCI NIH HHS/United States
R01 EB020426/EB/NIBIB NIH HHS/United States
Generated Citation:
Lee E., Zhu H., Kong D., Wang Y., Giovanello K.S., Ibrahim J.G. BFLCRM: A Bayesian Functional Linear Cox Regression Model for Predicting Time to Conversion to Alzheimer’s Disease. Ann Appl Stat. 2015 Dec;9(4):2153-2178. PMID: 26900412. PMCID: PMC4756762.
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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 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.

Additional Material
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