A Semiparametric Trajectory Model for Cognitive Decline

• Main Author: Li, Xiaoxia
• Format: Others
• Published: North Dakota State University 2017
• Online Access: http://hdl.handle.net/10365/25922

Dementia is a group of diseases that are caused by neurocognitive disorder. It is the second leading cause of death in older adults in the US. People who suffer from dementia experience memory loss and other cognitive or functional decline that is severe enough to interfere with their professional and social performance. In spite of the controversy on accuracy of diagnosis and debate on disclosure of dementia diagnosis results, it is important for patients and their families to know what to expect about the future development of cognitive decline. The course of dementia progression is highly diverse, and the symptoms vary differently from case to case. Amnesia, aphasia, agnosia, and apraxia can exist solely or in combination. The rate of cognitive decline, in the term of Clinical Dementia Rating Score, demonstrated different patterns on an individual level. However, in spite of the variety of symptoms, it is essential to map the cognitive decline to the severity of the impact of the symptoms on daily life. Clinical Dementia Rating SUM score (CDR SUM score) is a comprehensive evaluation based on cognition level. Trajectory modeling can provide a practical tool for physicians to make prognosis and medical trials. Furthermore, trajectory modeling can be a valuable reference for stakeholders to use in reimbursement decisions or policies on caregiving resource allocation. However, there is a gap in the current research to predict the trajectory for cognitive decline. In this research, we studied the typical pattern of CDR SUM scores and predicted a timeline for people with cognitive decline. The innovation and significance of this study is the development of multilevel and semiparametric models, and a simple and straightforward criterion for model evaluation and selection. The model we built showed robustness in both explaining the data and predictions. The study results revealed the factors associated with cognitive decline rate in terms of CDR SUM score, and gave implications on accurate CDR SUM score prediction by individual demographic and clinical profiles. The developed model can also be applied to other longitudinal studies in behavioral science, medical monitoring, and other time series related studies.