Semiparametric Frailty Models for Clustered Failure Time Data

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10 Scopus citations

Abstract

We consider frailty models with additive semiparametric covariate effects for clustered failure time data. We propose a doubly penalized partial likelihood (DPPL) procedure to estimate the nonparametric functions using smoothing splines. We show that the DPPL estimators could be obtained from fitting an augmented working frailty model with parametric covariate effects, whereas the nonparametric functions being estimated as linear combinations of fixed and random effects, and the smoothing parameters being estimated as extra variance components. This approach allows us to conveniently estimate all model components within a unified frailty model framework. We evaluate the finite sample performance of the proposed method via a simulation study, and apply the method to analyze data from a study of sexually transmitted infections (STI).

Original languageEnglish (US)
Pages (from-to)429-436
Number of pages8
JournalBiometrics
Volume68
Issue number2
DOIs
StatePublished - Jun 1 2012

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Keywords

  • Doubly penalized partial likelihood
  • Gaussian frailty
  • Sexually transmitted disease
  • Smoothing parameter
  • Smoothing spline
  • Variance components

ASJC Scopus subject areas

  • Applied Mathematics
  • Statistics and Probability
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Medicine(all)

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