Research Article

Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials

  1. Xiang Liu1,
  2. Yingwei Peng2,
  3. Dongsheng Tu2,
  4. Hua Liang1,*

Article first published online: 26 JUN 2012

DOI: 10.1002/sim.5378

Issue

Statistics in Medicine

Statistics in Medicine

Special Issue: Special Issue in Honor of Jerome Cornfield on the Centennial of His Birth

Volume 31, Issue 24, pages 2882–2891, 30 October 2012

Additional Information

How to Cite

Liu, X., Peng, Y., Tu, D. and Liang, H. (2012), Variable selection in semiparametric cure models based on penalized likelihood, with application to breast cancer clinical trials. Statist. Med., 31: 2882–2891. doi: 10.1002/sim.5378

Author Information

  1. 1

    Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, U.S.A.

  2. 2

    Department of Community Health and Epidemiology, Queen's University, Kingston, Ontario, Canada

* Hua Liang, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, U.S.A.
E-mail: hliang@bst.rochester.edu

Publication History

  1. Issue published online: 5 OCT 2012
  2. Article first published online: 26 JUN 2012
  3. Manuscript Accepted: 22 FEB 2012
  4. Manuscript Revised: 19 JAN 2012
  5. Manuscript Received: 25 AUG 2010

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Keywords:

  • Cox proportional hazards models;
  • EM algorithm;
  • generalized linear models;
  • LASSO;
  • penalized partial likelihood;
  • SCAD

Abstract

Survival data with a sizable cure fraction are commonly encountered in cancer research. The semiparametric proportional hazards cure model has been recently used to analyze such data. As seen in the analysis of data from a breast cancer study, a variable selection approach is needed to identify important factors in predicting the cure status and risk of breast cancer recurrence. However, no specific variable selection method for the cure model is available. In this paper, we present a variable selection approach with penalized likelihood for the cure model. The estimation can be implemented easily by combining the computational methods for penalized logistic regression and the penalized Cox proportional hazards models with the expectation–maximization algorithm. We illustrate the proposed approach on data from a breast cancer study. We conducted Monte Carlo simulations to evaluate the performance of the proposed method. We used and compared different penalty functions in the simulation studies. Copyright © 2012 John Wiley & Sons, Ltd.

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