Methodology and software for joint modelling of time-to-event data and longitudinal outcomes across multiple studies


Sudell, ME ORCID: 0000-0002-7919-4981 (2018) Methodology and software for joint modelling of time-to-event data and longitudinal outcomes across multiple studies PhD thesis, University of Liverpool.

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Abstract

Thesis Title: Methodology and Software for Joint Modelling of Time-to-Event Data and Longitudinal Outcomes Across Multiple Studies Author: Maria Sudell Introduction and Aims: Univariate joint models for longitudinal and time-to-event data simultaneously model one outcome that is repeatedly measured over time, with another outcome which measures the time until the occurrence of an event. They have been increasingly used in the literature to account for dropout in longitudinal studies, to include time-varying covariates in time-to-event analyses, or to investigate links between longitudinal and time-to-event outcomes. Meta-analysis is the quantitative pooling of data from multiple studies. Such analyses can provide increased sample size and so detect small covariate effects. Modelling of multi-study data requires accounting for the clustering of individuals within studies and careful consideration of heterogeneity between studies. Research concerning methodology for modelling of joint longitudinal and time-to-event data in a multi-study or meta-analytic setting does not currently exist. This thesis develops novel methodologies and software for the modelling of multi-study joint longitudinal and time-to-event ... (continues)

Item Type: Thesis (PhD)
Divisions: Faculty of Health & Life Sciences
Depositing User: Symplectic Admin
Date Deposited: 21 Aug 2018 10:48
Last Modified: 23 Aug 2025 18:58
DOI: 10.17638/03021706
Supervisors:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3021706
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