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Type: Thesis
Title: Longitudinal Data Analysis for Improving Patient Outcomes Following Hip Replacement Surgery
Author: Phipps, Bethany Pamela
Issue Date: 2019
School/Discipline: School of Mathematical Sciences
Abstract: Joint replacement surgery leaves many patients with postoperative pain and function limitations for extensive periods of time after surgery. This research will predict the likelihood of poor symptomatic recovery following surgery using preoperative patient data, including data on patient age, sex and comorbidities. The dataset to be analysed is total hip replacement data collected between 1989 and 2013 at the Royal Adelaide Hospital. Using the pain and function data collected repeatedly after surgery, longitudinal data analysis will be explored. The mortality information in the data will be used to explore the survival probability of patients based on different predictors using survival analysis. Repeated pain and function outcomes are modelled using mixed-effects modelling. The joint modelling of both survival and longitudinal models will be developed. Prediction methods surrounding these models will be compared to help assess the potential benefits of total hip replacement surgery for patients prior to surgery.
Advisor: Tuke, Jono
Glonek, Gary
Dissertation Note: Thesis (MPhil) -- University of Adelaide, School of Mathematical Sciences, 2019
Keywords: Longitudinal data analysis
hip replacement
mixed effects modeling
joint modeling
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
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