A Bayesian Evaluation of Subtyping Methods in Parkinson’s Disease
| dc.contributor.author | Johnson, Andrew Robert | |
| dc.contributor.supervisor | Natalie Gasson | en_US |
| dc.contributor.supervisor | Andrea Loftus | en_US |
| dc.date.accessioned | 2020-05-14T09:25:35Z | |
| dc.date.available | 2020-05-14T09:25:35Z | |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | http://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.publisher | Curtin University | en_US |
| dc.title | A Bayesian Evaluation of Subtyping Methods in Parkinson’s Disease | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | School of Psychology | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Health Sciences | en_US |