Abstract
We propose a mixture model that combines a discrete-time survival model for analyzing the correlated times between recurrent events, e.g. births, with a logistic regression model for the probability of never experiencing the event of interest, i.e., being a long-term survivor. The proposed survival model incorporates both observed and unobserved heterogeneity in the probability of experiencing the event of interest. We use Gibbs sampling for the fitting of such mixture models, which leads to a computationally intensive solution to the problem of fitting survival models for multiple event time data with long-term survivors. We illustrate our Bayesian approach through an analysis of Hutterite birth histories.
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McDonald, J.W., Rosina, A. Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals. Statistical Methods & Applications 10, 257–272 (2001). https://doi.org/10.1007/BF02511651
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DOI: https://doi.org/10.1007/BF02511651