TY  - UNPB
N2  - Health data often give rise to complex survival outcomes, which cannot be dealt
with using traditional methods without incurring a loss of crucial information. We
consider four such cases, motivated by different clinical settings, and present, for
each, a general and flexible modelling framework, with the aim of achieving a better
understanding of disease patterns and more accurate predictions.
We first focus on diseases which manifest through multiple organs, resulting
in dependent time-to-events. We propose a copula-based framework for the joint
modelling of bivariate survival outcomes, specified as flexible functions of time and
the covariates of interest, with a mixed censoring scheme.
When interest lies in the progression of a disease, multi-state processes represent
a powerful modelling approach. For the second case, we consider a continuously
observed process, and propose a unified framework that exploits the simplification
implied by the exact knowledge of the times-to-events. It combines the flexible
specification of each transition, with a simulation-based approach to compute the
transition probabilities, posing no limitations on the processes supported.
When constant monitoring of the process is not possible, existing models do
not allow the information contained in the intermittently-observed data thus limiting
the specifications supported to be fully exploited. The third framework proposed
overcomes this challenge by exploiting a novel development, i.e. a closed-form
expression for the local curvature information of the transition probability matrix,
and supports flexible modelling for virtually any type of process.
Finally, we develop an approach to model two dependent multi-state processes.
This is motivated by clinical applications which give rise to two (or more) associated diseases, making the modelling of their joint progression of interest.
The frameworks described are implemented in the R packages GJRM and
flexmsm and are exemplified through case studies based on clinical data.
UR  - https://discovery.ucl.ac.uk/id/eprint/10194838/
ID  - discovery10194838
EP  - 223
M1  - Doctoral
Y1  - 2024/07/28/
TI  - General flexible modelling frameworks for multivariate and multi-state survival outcomes
AV  - public
PB  - UCL (University College London)
A1  - Eletti, Alessia
N1  - Copyright © The Author 2024.  Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/).  Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms.  Access may initially be restricted at the author?s request.
ER  -