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dc.contributor.authorJohnson, Andrew Robert
dc.contributor.supervisorNatalie Gassonen_US
dc.contributor.supervisorAndrea Loftusen_US
dc.date.accessioned2020-05-14T09:25:35Z
dc.date.available2020-05-14T09:25:35Z
dc.date.issued2019en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11937/79166
dc.description.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.

en_US
dc.publisherCurtin Universityen_US
dc.titleA Bayesian Evaluation of Subtyping Methods in Parkinson’s Diseaseen_US
dc.typeThesisen_US
dcterms.educationLevelPhDen_US
curtin.departmentSchool of Psychologyen_US
curtin.accessStatusOpen accessen_US
curtin.facultyHealth Sciencesen_US


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