Hardcastle, Luke;
Livingstone, Samuel;
Baio, Gianluca;
(2025)
Averaging polyhazard models using Piecewise deterministic Monte Carlo with applications to data with long-term survivors.
Annals of Applied Statistics
, 19
(4)
pp. 3179-3202.
10.1214/25-AOAS2064.
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Abstract
Polyhazard models are a class of flexible parametric models for modelling survival over extended time horizons. Their additive hazard structure allows for flexible, nonproportional hazards whose characteristics can change over time while retaining a parametric form, which allows for survival to be extrapolated beyond the observation period of a study. Significant user input is required, however, in selecting the number of latent hazards to model, their distributions and the choice of which variables to associate with each hazard. The resulting set of models is too large to explore manually, limiting their practical usefulness. Motivated by applications to stroke survivor and kidney transplant patient survival times, we extend the standard polyhazard model through a prior structure, allowing for joint inference of parameters and structural quantities, and develop a sampling scheme that utilises state-of-the-art piecewise deterministic Markov processes to sample from the resulting transdimensional posterior with minimal user tuning.
| Type: | Article |
|---|---|
| Title: | Averaging polyhazard models using Piecewise deterministic Monte Carlo with applications to data with long-term survivors |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1214/25-AOAS2064 |
| Publisher version: | https://doi.org/10.1214/25-AOAS2064 |
| Language: | English |
| Additional information: | This research was funded, in whole or in part, by [UK Engineering and Physical Sciences Research Council, ES/W52385/1]. A CC BY 4.0 license is applied to this article arising from this submission, in accordance with the grant’s open access conditions. |
| Keywords: | Bayesian model averaging, health technology assessment, Markov chain Monte Carlo, Piecewise deterministic Markov processes, Polyhazard models, Survival analysis |
| 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/10210147 |
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