Liu, Xinyi;
(2023)
Joint Modelling of Competing Risks and Time-Dependent Covariates.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
In this thesis we propose a joint model for competing risks and longitudinal data. Our joint model provides a flexible approach to handle longitudinal data with complicated structures. Our model consists of a multi-state model for the competing risks and a general mixed model for the longitudinal outcomes, linked together by some latent random effects. For the joint model of one longitudinal outcome, we obtain the estimates of the parameters by maximising the marginal likelihood. We also extend the joint model to take into account multiple longitudinal outcomes simultaneously. To alleviate the 'curse of dimensionality' in integration, we propose to use Bayesian inference and use the posterior means as the estimates of the parameters. The joint models are applied to two datasets, the English Longitudinal Study of Ageing (ELSA) and the clinical data from the PhysioNet/Computing in Cardiology Challenge 2019. For the second dataset, we also propose a two-stage framework for disease early diagnosis. We construct a time-dependent loss function, and make diagnosis by minimising the expected loss.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Joint Modelling of Competing Risks and Time-Dependent Covariates |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | © The Author(s). This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10173420 |
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