Bayesian Autoregressive Frailty Models for Inference in Recurrent Events

Tallarita Marta (), Maria De Iorio (), Guglielmi Alessandra () and Malone-Lee James ()
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Tallarita Marta: Department of Statistical Science, University College London, London, UK
Maria De Iorio: Department of Statistical Science, University College London, London, UK
Guglielmi Alessandra: Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
Malone-Lee James: Division of Medicine, University College London, London, UK

The International Journal of Biostatistics, 2020, vol. 16, issue 1, 18

Abstract: We propose autoregressive Bayesian semi-parametric models for gap times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the frailty parameters influencing the response at different times. The order of the autoregression may be assumed unknown and is an object of inference. We consider two alternative approaches to perform model selection under this scenario. Covariates may be easily included in the regression framework and censoring and missing data are easily accounted for. As the proposed methodologies lie within the class of Dirichlet process mixtures, posterior inference can be performed through efficient MCMC algorithms. We illustrate the approach through simulations and medical applications involving recurrent hospitalizations of cancer patients and successive urinary tract infections.

Keywords: autoregressive models; Dirichlet process mixtures; model selection (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1515/ijb-2018-0088

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