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A semiparametric multilevel survival model

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  • Wenyang Zhang
  • Fiona Steele

Abstract

Summary. We propose a semiparametric multilevel survival model for clustered duration data in which the effect of a continuous covariate is represented by an unspecified, possibly non‐linear, function. This model makes no distributional assumption about the cluster level random effects. The performance of the method is assessed via Monte Carlo simulations. The model is applied in an analysis of first‐birth intervals in Bangladesh to examine period effects in the timing of first births, while allowing for clustering within communities; the analysis reveals a non‐linear trend in the first‐birth interval over time.

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  • Wenyang Zhang & Fiona Steele, 2004. "A semiparametric multilevel survival model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(2), pages 387-404, April.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:2:p:387-404
    DOI: 10.1111/j.1467-9876.2003.05056.x
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    Cited by:

    1. Changrong Yan & Dixin Zhang, 2013. "Sparse dimension reduction for survival data," Computational Statistics, Springer, vol. 28(4), pages 1835-1852, August.
    2. Lawrence Kazembe, 2009. "Modelling individual fertility levels in Malawian women: a spatial semiparametric regression model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 18(2), pages 237-255, July.
    3. Ezra Gayawan & Samson B. Adebayo, 2013. "A Bayesian semiparametric multilevel survival modelling of age at first birth in Nigeria," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 28(45), pages 1339-1372.

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