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Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals

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  • John W. McDonald

    (University of Southampton)

  • Alessandro Rosina

    (Catholic University-Milan)

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.

Suggested Citation

  • John W. McDonald & Alessandro Rosina, 2001. "Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 10(1), pages 257-272, January.
    • Handle: RePEc:spr:stmapp:v:10:y:2001:i:1:d:10.1007_bf02511651
    DOI: 10.1007/BF02511651
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    References listed on IDEAS

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    1. Ulla Larsen & James Vaupel, 1993. "Hutterite fecundability by age and parity: Strategies for frailty modeling of event histories," Demography, Springer;Population Association of America (PAA), vol. 30(1), pages 81-102, February.
    2. Lei Li & Minja Choe, 1997. "A mixture model for duration data: Analysis of second births in China," Demography, Springer;Population Association of America (PAA), vol. 34(2), pages 189-197, May.
    3. Nber, 1965. "Studies New and Completed," NBER Chapters, in: The Task of Economics, pages 33-37, National Bureau of Economic Research, Inc.
    4. Duolao Wang & Mike Murphy, 1998. "Use of a mixture model for the analysis of contraceptive-use duration among long-term users," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(3), pages 319-332.
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    Cited by:

    1. Zhao, Xiaobing & Zhou, Xian, 2012. "Modeling gap times between recurrent events by marginal rate function," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 370-383.
    2. Vincent Bremhorst & Michaela Kreyenfeld & Philippe Lambert, 2016. "Fertility progression in Germany: An analysis using flexible nonparametric cure survival models," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 35(18), pages 505-534.
    3. Ali Sharifi & Reza Hashemi, 2023. "Semiparametric Model for Recurrent Event Data Under Two Independent Competing Risks with Excess Zero and Informative Censoring," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 633-650, February.
    4. Eva Beaujouan & Anne Solaz, 2013. "Racing Against the Biological Clock? Childbearing and Sterility Among Men and Women in Second Unions in France," European Journal of Population, Springer;European Association for Population Studies, vol. 29(1), pages 39-67, February.
    5. Jona Schellekens, 2009. "Family allowances and fertility: Socioeconomic differences," Demography, Springer;Population Association of America (PAA), vol. 46(3), pages 451-468, August.
    6. Francisco Louzada & Juliana Cobre, 2012. "A multiple time scale survival model with a cure fraction," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(2), pages 355-368, June.

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