A semiparametric recurrent events model with time-varying coefficients

Zhangsheng Yu, Lei Liu, Dawn M. Bravata, Linda S. Williams, Robert S. Tepper

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


We consider a recurrent events model with time-varying coefficients motivated by two clinical applications. We use a random effects (Gaussian frailty) model to describe the intensity of recurrent events. The model can accommodate both time-varying and time-constant coefficients. We use the penalized spline method to estimate the time-varying coefficients. We use Laplace approximation to evaluate the penalized likelihood without a closed form. We estimate the smoothing parameters in a similar way to variance components. We conduct simulations to evaluate the performance of the estimates for both time-varying and time-independent coefficients. We apply this method to analyze two data sets: a stroke study and a child wheeze study.

Original languageEnglish
Pages (from-to)1016-1026
Number of pages11
JournalStatistics in medicine
Issue number6
StatePublished - Mar 15 2013


  • Penalized spline
  • Semiparametric regression
  • Survival analysis
  • Variance components


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