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arXiv · 2308.10583

The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis

Willem van den Boom·Maria De Iorio·Fang Qian·Alessandra Guglielmi
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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 suffer 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 non-conjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.

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Willem van den Boom, Maria De Iorio, Fang Qian, Alessandra Guglielmi. 2023-08-21. The Multivariate Bernoulli detector: Change point estimation in discrete survival analysis. https://doi.org/10.1093/biomtc%2Fujae075

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