Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals
John W. McDonald () and
Alessandro Rosina ()
Additional contact information
John W. McDonald: University of Southampton
Alessandro Rosina: Catholic University-Milan
Statistical Methods & Applications, 2001, vol. 10, issue 1, No 18, 257-272
Abstract:
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.
Keywords: Discrete-time survival model; long-term survivors; mixture model; multivariate survival data; recurrent events (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)
Downloads: (external link)
http://link.springer.com/10.1007/BF02511651 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:stmapp:v:10:y:2001:i:1:d:10.1007_bf02511651
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10260/PS2
DOI: 10.1007/BF02511651
Access Statistics for this article
Statistical Methods & Applications is currently edited by Tommaso Proietti
More articles in Statistical Methods & Applications from Springer, Società Italiana di Statistica
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().