Bayesian inference in survival analysis: new approaches to modelling and computation

Survival models are ubiquitous in many application areas of data science, in particular in modelling data arising from clinical trials but also increasingly from observational studies, in which data are more freely available but typically also more structured and heterogeneous. To accurately learn from observational data more nuanced and complex modelling strategies must be taken, which in turn makes robust and reliable inference a challenge. The first aim of this project are to develop fit-for-purpose survival models for use with modern datasets used in health economic evaluation. Following this, we will develop robust and scalable inference algorithms for these models, borrowing ideas from and building upon state-of-the-art approaches used in Bayesian computation. The project will be done in consultation and collaboration with numerous industrial partners, such as researchers at ICON Plc, a global consultancy company working with major pharmaceutical companies and government bodies, to ensure that any methods developed are both fit-for-purpose and fall within regulatory frameworks. To complete the project bespoke software packages will be developed to provide an end-to-end data science pipeline for robust Bayesian survival modelling.

Research Areas:
Statistics and applied probability Operational Research