The multivariate Bernoulli detector: change point estimation in discrete survival analysis

Author:

van den Boom Willem1ORCID,De Iorio Maria12ORCID,Qian Fang1,Guglielmi Alessandra3ORCID

Affiliation:

1. Yong Loo Lin School of Medicine, National University of Singapore , Singapore 119228 , Singapore

2. Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research , Singapore 117609 , Singapore

3. Department of Mathematics, Politecnico di Milano , Milan 20133 , Italy

Abstract

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.

Funder

National Medical Research Council

European Union

Publisher

Oxford University Press (OUP)

Reference46 articles.

1. Discrete-time methods for the analysis of event histories;Allison;Sociological Methodology,1982

2. Competing risks in epidemiology: possibilities and pitfalls;Andersen;International Journal of Epidemiology,2012

3. Lasso meets horseshoe: a survey;Bhadra;Statistical Science,2019

4. Using flexible noise models to avoid noise model misspecification in inference of differential equation time series models;Creswell,2020

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