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Bayesian inference on the number of recurrent events: A joint model of recurrence and survival

van den Boom, Willem; De Iorio, Maria; Tallarita, Marta; (2022) Bayesian inference on the number of recurrent events: A joint model of recurrence and survival. Statistical Methods in Medical Research , 31 (1) pp. 139-153. 10.1177/09622802211048059. Green open access

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Abstract

The number of recurrent events before a terminating event is often of interest. For instance, death terminates an individual’s process of rehospitalizations and the number of rehospitalizations is an important indicator of economic cost. We propose a model in which the number of recurrences before termination is a random variable of interest, enabling inference and prediction on it. Then, conditionally on this number, we specify a joint distribution for recurrence and survival. This novel conditional approach induces dependence between recurrence and survival, which is often present, for instance, due to frailty that affects both. Additional dependence between recurrence and survival is introduced by the specification of a joint distribution on their respective frailty terms. Moreover, through the introduction of an autoregressive model, our approach is able to capture the temporal dependence in the recurrent events trajectory. A non-parametric random effects distribution for the frailty terms accommodates population heterogeneity and allows for data-driven clustering of the subjects. A tailored Gibbs sampler involving reversible jump and slice sampling steps implements posterior inference. We illustrate our model on colorectal cancer data, compare its performance with existing approaches and provide appropriate inference on the number of recurrent events.

Type: Article
Title: Bayesian inference on the number of recurrent events: A joint model of recurrence and survival
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/09622802211048059
Publisher version: https://doi.org/10.1177/09622802211048059
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Health Care Sciences & Services, Mathematical & Computational Biology, Medical Informatics, Statistics & Probability, Mathematics, Accelerated failure time model, censoring, colorectal cancer, Dirichlet process mixtures, hospital readmission cost burden, number of recurrent events, reversible jump Markov chain Monte Carlo, FRAILTY MODELS, SEMIPARAMETRIC ANALYSIS, DEPENDENT TERMINATION, HOSPITAL READMISSION
UCL classification: 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
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10158468
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