Show simple item record

dc.contributor.authorJohnson, Andrew Robert
dc.contributor.supervisorNatalie Gassonen_US
dc.contributor.supervisorAndrea Loftusen_US

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.

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

Files in this item


This item appears in the following Collection(s)

Show simple item record