Statistical methods for modelling falls and symptoms progression in patients with early stages of Parkinson's disease
|
Sarini Thesis (PDF 13MB) |
Description
This thesis was a step forward in gaining insight into falls in people with early stages of Parkinson's disease (PD), and in monitoring the disease progression based on clinical assessments. This research contributes new knowledge by providing new insights into utilizing information provided by the clinically administered instruments used routinely for the assessment of PD severity. The novel approach to modelling the progression of PD symptoms using multi-variable clinical assessment measurements for longitudinal data provides a new perspective into disease progression.
Impact and interest:
Citation counts are sourced monthly from Scopus and Web of Science® citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads:
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
| ID Code: | 116208 |
|---|---|
| Item Type: | QUT Thesis (PhD) |
| Supervisor: | McGree, James, Mengersen, Kerrie, Kerr, Graham, & White, Nicole |
| Additional Information: | The Faculty has granted an embargo until the 18th of January, 2020. |
| Keywords: | Autoregressive, Bayesian variable selection, Bayesian model averaging, Clinical assessments, Decision tree, Deviance information criterion, Disease progression, Falls incidence, Falls frequency, Finite mixture model |
| DOI: | 10.5204/thesis.eprints.116208 |
| Divisions: | Past > QUT Faculties & Divisions > Faculty of Health Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Mathematical Sciences Current > Schools > School of Exercise & Nutrition Sciences Current > Schools > School of Public Health & Social Work |
| Institution: | Queensland University of Technology |
| Deposited On: | 07 Jun 2018 04:24 |
| Last Modified: | 31 May 2021 14:44 |
Export: EndNote | Dublin Core | BibTeX
Repository Staff Only: item control page