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Semiparametric frailty models for clustered failure time data
Authors:Yu Zhangsheng  Lin Xihong  Tu Wanzhu
Institution:Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA. yuz@iupui.edu
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).
Keywords:Doubly penalized partial likelihood  Gaussian frailty  Sexually transmitted disease  Smoothing parameter  Smoothing spline  Variance components
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