Semiparametric Frailty Models for Clustered Failure Time Data
Article first published online: 9 NOV 2011
© 2011, The International Biometric Society
Volume 68, Issue 2, pages 429–436, June 2012
How to Cite
Yu, Z., Lin, X. and Tu, W. (2012), Semiparametric Frailty Models for Clustered Failure Time Data. Biometrics, 68: 429–436. doi: 10.1111/j.1541-0420.2011.01683.x
- Issue published online: 26 JUN 2012
- Article first published online: 9 NOV 2011
- Received July 2010. Revised August 2011. Accepted August 2011.
- Doubly penalized partial likelihood;
- Gaussian frailty;
- Sexually transmitted disease;
- Smoothing parameter;
- Smoothing spline;
- Variance components
Summary 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).