A Bayesian Evaluation of Subtyping Methods in Parkinson’s Disease
Access Status
Open access
Authors
Johnson, Andrew Robert
Date
2019Supervisor
Natalie Gasson
Andrea Loftus
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Health Sciences
School
School of Psychology
Collection
Abstract
Parkinson’s disease (PD) has significant heterogeneity in its presentation. To explain this heterogeneity, several motor subtypes have been proposed. These subtypes make assumptions about how symptoms change over time, the ability to measure symptoms, and the relationships between different symptoms within a given disease subtype. However, current statistical approaches cannot test these assumptions. This thesis used Bayesian statistics to evaluate the assumptions underlying current subtyping methods and developed a new model of PD motor subtypes.
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