Powney, Matthew
(2015)
Joint modelling of longitudinal and time-to-event data.
PhD thesis, University of Liverpool.
|
Text
PowneyMat_Oct2015_2053320 edited.pdf - Unspecified Download (16MB) | Preview |
Abstract
A randomised control trial (RCT) is considered to be the gold standard for investigating the efficacy of new and novel treatments. However, in RCTs with longitudinal outcomes and high percentages of dropout, a poor handing of missing data can be problematic when trying to establishing the efficacy of an intervention. Joint modelling of longitudinal and time-to-event data is a novel methodology that can be used to monitor a longitudinal outcome while simultaneously accounting for time-to-dropout. This is achieved using a mean zero latent Gaussian process, and relies on the estimation of the parameter gamma, which models the association between the longitudinal and time-to-event components. However, joint modelling is still a relatively new topic for research. The aim of this thesis is to provide and develop a greater understanding for both the design and analysis elements of joint modelling. In Chapter 2 a simulation study to test the success of various missing data handling methods is presented. This demonstrated that for RCTs with missing data, joint modelling performs as well as the common alternative methods when estimating longitudinal treatment effect. Despite these benefits, a systematic review conducted ... (continues)
| Item Type: | Thesis (PhD) |
|---|---|
| Additional Information: | Date: 2015-10-23 (completed) |
| Divisions: | Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences > School of Medicine |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 14 Jul 2020 09:45 |
| Last Modified: | 08 Feb 2025 01:02 |
| DOI: | 10.17638/02053320 |
| Supervisors: |
|
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3093967 |
Altmetric
Altmetric