Autoregressive Bayesian semi-parametric models for gap times between recurrent events

What is it about?

We cluster individuals based on the time trajectory of the recurrent event through the statistical model. 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.

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Why is it important?

The order of the autoregression in the frailty parameter distribution may be assumed unknown and is an object of inference. 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.