Corpus ID: 218628900

Bayesian Joint Modelling of Recurrence and Survival: a Conditional Approach.

@article{Boom2020BayesianJM,
  title={Bayesian Joint Modelling of Recurrence and Survival: a Conditional Approach.},
  author={Willem van den Boom and Marta Tallarita and Maria De Iorio},
  journal={arXiv: Methodology},
  year={2020}
}
  • Willem van den Boom, Marta Tallarita, Maria De Iorio
  • Published 2020
  • Mathematics
  • arXiv: Methodology
  • Recurrent event processes describe the stochastic repetition of an event over time. Recurrent event times are often censored with dependence between the censoring time and recurrence process. For instance, recurrent disease events are censored by a terminal event such as death, while frailty might affect both disease recurrence and survival. As such, it is important to model the recurrent event process and the event time process jointly to better capture the dependency between them and improve… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 20 REFERENCES
    Joint modeling of recurrent events and survival: a Bayesian non-parametric approach.
    2
    Bayesian Nonparametric Modelling of Joint Gap Time Distributions for Recurrent Event Data
    3
    Semiparametric analysis for recurrent event data with time-dependent covariates and informative censoring.
    51
    A joint frailty model for survival and gap times between recurrent events.
    76
    Bayesian Model-Based Clustering Procedures
    187
    Fast and accurate computation of the distribution of sums of dependent log-normals
    8
    The Statistical Analysis of Recurrent Events
    519
    Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
    5339
    Markov Chain Sampling Methods for Dirichlet Process Mixture Models
    1228