ArticleThe annals of applied statistics2015
BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE.
Article in The annals of applied statistics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment.The Cochrane database of systematic reviews · 2020Pooled it
- Shape-Based Partially Linear Single-Index Cox Model for Alzheimer's Disease Conversion.Statistics in medicine · 2026Article
- Functional varying-coefficient Cox model and its application.Statistical methods in medical research · 2026Article
- Bayesian Estimation of Partial Functional Tobit Censored Quantile Regression Model.Statistics in medicine · 2025Article
- Characterizing bidirectional transitions in mild cognitive impairment and post-reversion based on longitudinal neuroimaging and cognitive assessments.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Supervised Functional Principal Component Analysis Under the Mixture Cure Rate Model: An Application to Alzheimer'S Disease.Statistics in medicine · 2025Article
- Prognostic model for predicting Alzheimer's disease conversion using functional connectome manifolds.Alzheimer's research & therapy · 2024Article
- Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data.Medical image analysis · 2021Article
- Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study.Biometrics · 2020Article
- Bayesian hidden Markov models for delineating the pathology of Alzheimer's disease.Statistical methods in medical research · 2019Article
- Organizing and analyzing the activity data in NHANES.Statistics in biosciences · 2019Article
- Bayesian adaptive group lasso with semiparametric hidden Markov models.Statistics in medicine · 2019Article
- Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease.Statistical methods in medical research · 2019Article
- Using Multi-Scale Genetic, Neuroimaging and Clinical Data for Predicting Alzheimer's Disease and Reconstruction of Relevant Biological Mechanisms.Scientific reports · 2018Article
- FLCRM: Functional linear cox regression model.Biometrics · 2018Article
- Functional joint model for longitudinal and time-to-event data: an application to Alzheimer's disease.Statistics in medicine · 2017Article
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6 authors.
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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 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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