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A Bayesian model of psychosis symptom trajectory in Alzheimer's disease
Author(s) -
Seltman Howard J.,
Mitchell Shaina,
Sweet Robert A.
Publication year - 2016
Publication title -
international journal of geriatric psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.28
H-Index - 129
eISSN - 1099-1166
pISSN - 0885-6230
DOI - 10.1002/gps.4326
Subject(s) - psychosis , cognition , dementia , psychology , covariate , psychiatry , cognitive decline , affect (linguistics) , logistic regression , schizophrenia (object oriented programming) , disease , clinical psychology , medicine , statistics , mathematics , communication , pathology
Objective Psychosis, like other neuropsychiatric symptoms of dementia, has many features that make predictive modeling of its onset difficult. For example, psychosis onset is associated with both the absolute degree of cognitive impairment and the rate of cognitive decline. Moreover, psychotic symptoms, while more likely than not to persist over time within individuals, may remit and recur. To facilitate predictive modeling of psychosis for personalized clinical decision making, including evaluating the role of risk genes in its onset, we have developed a novel Bayesian model of the dual trajectories of cognition and psychosis symptoms. Methods Cognition was modeled as a four‐parameter logistic curve with random effects for all four parameters and possible covariates for the rate and time of fall. Psychosis was modeled as a continuous‐time hidden Markov model with a latent never‐psychotic class and states for pre‐psychotic, actively psychotic and remitted psychosis. Covariates can affect the probability of being in the never‐psychotic class. Covariates and the level of cognition can affect the transition rates for the hidden Markov model. Results The model characteristics were confirmed using simulated data. Results from 434 AD patients show that a decline in cognition is associated with an increased rate of transition to the psychotic state. Conclusions The model allows declining cognition as an input for psychosis prediction, while incorporating the full uncertainty of the interpolated cognition values. The techniques used can be used in future genetic studies of AD and are generalizable to the study of other neuropsychiatric symptoms in dementia. Copyright © 2015 John Wiley & Sons, Ltd.