ArticleStatistical methods in medical research2021
A Bayesian hierarchical change point model with parameter constraints.
Article in Statistical methods in medical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Estimating the baseline age when longitudinal changes in biomarkers accelerate: application to an imaging study of preclinical Alzheimer disease.BMC medical research methodology · 2026Article
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Abstract
Alzheimer's disease is the leading cause of dementia among adults aged 65 or above. Alzheimer's disease is characterized by a change point signaling a sudden and prolonged acceleration in cognitive decline. The timing of this change point is of clinical interest because it can be used to establish optimal treatment regimens and schedules. Here, we present a Bayesian hierarchical change point model with a parameter constraint to characterize the rate and timing of cognitive decline among Alzheimer's disease patients. We allow each patient to have a unique random intercept, random slope before the change point, random change point time, and random slope after the change point. The difference in slope before and after a change point is constrained to be nonpositive, and its parameter space is partitioned into a null region (representing normal aging) and a rejection region (representing accelerated decline). Using the change point time, the estimated slope difference, and the threshold of the null region, we are able to (1) distinguish normal aging patients from those with accelerated cognitive decline, (2) characterize the rate and timing for patients experiencing cognitive decline, and (3) predict personalized risk of progression to dementia due to Alzheimer's disease. We apply the approach to data from the Religious Orders Study, a national cohort study of aging Catholic nuns, priests, and lay brothers.
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