Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals |
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Authors: | John W McDonald Alessandro Rosina |
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Institution: | (1) Department of Social Statistics, University of Southampton, S017 1BJ Southampton, UK;(2) Institute of Population and Geographical Studies, Catholic University-Milan, Via Largo Gemelli, 1, 20123 Milano, Italy |
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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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Keywords: | Discrete-time survival model long-term survivors mixture model multivariate survival data recurrent events |
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