Che, Zhaojing;
(2025)
Bayesian survival modelling in health economic evaluation.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Economic evaluations as part of health technology assessments typically require estimates of lifetime survival benefit for new oncologic therapies. Interim analyses of trials with limited follow-up are increasingly used to inform regulatory approval, but the high degrees of administrative censoring in these trials create significant challenges when it comes time to extrapolate survival outcomes over a lifetime time horizon. Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinitely, which is unrealistic and may have a great impact on decisions for resource allocation. This thesis starts by presenting an introduction to the survival analysis for economic evaluations, specifically the advantages of Bayesian modelling structure. I investigate the existing methods to estimate long-term survival benefit in the presence of heavily censored data, and their main limitations and implications for the wider economic analysis where evidence synthesis is required across clinical trials. Then, a novel methodology based on “blending” survival curves is proposed as a possible solution to alleviate the underlying problem of survival extrapolations. Finally, I demonstrate the benefits of our approach using two case studies. The blended survival curve provides a simple and powerful framework to allow a careful consideration of a wide range of plausible scenarios, accounting for the model fit to the short-term data as well as the plausibility of long-term extrapolations.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Bayesian survival modelling in health economic evaluation |
| Language: | English |
| Additional information: | Copyright © The Author 2025. 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. |
| 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.ucl.ac.uk/id/eprint/10215651 |
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