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The multivariate Bernoulli detector: change point estimation in discrete survival analysis.
- Source :
-
Biometrics [Biometrics] 2024 Jul 01; Vol. 80 (3). - Publication Year :
- 2024
-
Abstract
- Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.<br /> (© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society.)
Details
- Language :
- English
- ISSN :
- 1541-0420
- Volume :
- 80
- Issue :
- 3
- Database :
- MEDLINE
- Journal :
- Biometrics
- Publication Type :
- Academic Journal
- Accession number :
- 39136277
- Full Text :
- https://doi.org/10.1093/biomtc/ujae075