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    A Bayesian Evaluation of Subtyping Methods in Parkinson’s Disease

    Johnson A 2019.pdf (2.466Mb)
    Access Status
    Open access
    Authors
    Johnson, Andrew Robert
    Date
    2019
    Supervisor
    Natalie Gasson
    Andrea Loftus
    Type
    Thesis
    Award
    PhD
    
    Metadata
    Show full item record
    Faculty
    Health Sciences
    School
    School of Psychology
    URI
    http://hdl.handle.net/20.500.11937/79166
    Collection
    • Curtin Theses
    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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