ArticleStatistical methods in medical research2019
Bayesian hidden Markov models for delineating the pathology of Alzheimer's disease.
Article in Statistical methods in medical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 31 citations in OpenAlex.
- Shape-Based Partially Linear Single-Index Cox Model for Alzheimer's Disease Conversion.Statistics in medicine · 2026Article
- Predictors of Cognitive Decline in Alzheimer's Disease: A Longitudinal Bayesian Analysis.Medicina (Kaunas, Lithuania) · 2025Article
- Improved Alzheimer Disease Diagnosis With a Machine Learning Approach and Neuroimaging: Case Study Development.JMIRx med · 2025Article
- Estimating Transition Probabilities Across the Alzheimer's Disease Continuum Using a Nationally Representative Real-World Database in the United States.Neurology and therapy · 2023Article
- A Review on Phyto-Therapeutic Approaches in Alzheimer's Disease.Journal of functional biomaterials · 2023Review
- A Novel Coupling Model of Physiological Degradation and Emotional State for Prediction of Alzheimer's Disease Progression.Brain sciences · 2022Article
- Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes.Lifetime data analysis · 2022Article
- Bayesian adaptive group lasso with semiparametric hidden Markov models.Statistics in medicine · 2019Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
Alzheimer's disease is a firmly incurable and progressive disease. The pathology of Alzheimer's disease usually evolves from cognitive normal, to mild cognitive impairment, to Alzheimer's disease. The aim of this paper is to develop a Bayesian hidden Markov model to characterize disease pathology, identify hidden states corresponding to the diagnosed stages of cognitive decline, and examine the dynamic changes of potential risk factors associated with the cognitive normal-mild cognitive impairment-Alzheimer's disease transition. The hidden Markov model framework consists of two major components. The first one is a state-dependent semiparametric regression for delineating the complex associations between clinical outcomes of interest and a set of prognostic biomarkers across neurodegenerative states. The second one is a parametric transition model, while accounting for potential covariate effects on the cross-state transition. The inter-individual and inter-process differences are taken into account via correlated random effects in both components. Based on the Alzheimer's Disease Neuroimaging Initiative data set, we are able to identify four states of Alzheimer's disease pathology, corresponding to common diagnosed cognitive decline stages, including cognitive normal, early mild cognitive impairment, late mild cognitive impairment, and Alzheimer's disease and examine the effects of hippocampus, age, gender, and APOE-
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.