Lythgoe, Daniel 
ORCID: 0000-0002-4682-772X
(2020)
Time-to-event latent variable models for the statistical analysis of clinical data.
PhD thesis, University of Liverpool.
|
Text
201001743_Mar2020.pdf - Unspecified Download (1MB) | Preview |
Abstract
In clinical research, interest sometimes lies in analysing variables which are not measured directly. Instead, information about these ‘latent variables’ can be inferred from surrogates or other imperfect indicators, using latent variable models. Common examples of ‘hypothetical’ latent variables in clinical research include quality of life (QoL), anxiety and depression. Another type of latent variable is a variable used as a device for dimension reduction, for example, a principal component. The aim of this thesis is to explore and develop latent variable methods for the statistical analysis of clinical data, with an emphasis on including latent variables in time-to-event models.
| Item Type: | Thesis (PhD) |
|---|---|
| Divisions: | Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences > School of Medicine |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 09 Apr 2020 14:01 |
| Last Modified: | 18 Jan 2023 23:59 |
| DOI: | 10.17638/03077807 |
| Supervisors: |
|
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3077807 |
Dimensions
Dimensions
