Penalized likelihood in Cox regression

Penalized likelihood in Cox regression In a Cox regression model, instability of the estimated regression coefficients can be reduced by maximizing a penalized partial log‐likelihood, where a penalty function of the regression coefficients is substracted from the partial log‐likelihood. In this paper, we choose the optimal weight of the penalty function by maximizing the predictive value of the model, as measured by the crossvalidated partial log‐likelihood. Our methods are illustrated by a study of ovarian cancer survival and by a study of centre effects in kidney graft survival. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics in Medicine Wiley

Penalized likelihood in Cox regression

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Publisher
Wiley
Copyright
Copyright © 1994 John Wiley & Sons, Ltd.
ISSN
0277-6715
eISSN
1097-0258
D.O.I.
10.1002/sim.4780132307
Publisher site
See Article on Publisher Site

Abstract

In a Cox regression model, instability of the estimated regression coefficients can be reduced by maximizing a penalized partial log‐likelihood, where a penalty function of the regression coefficients is substracted from the partial log‐likelihood. In this paper, we choose the optimal weight of the penalty function by maximizing the predictive value of the model, as measured by the crossvalidated partial log‐likelihood. Our methods are illustrated by a study of ovarian cancer survival and by a study of centre effects in kidney graft survival.

Journal

Statistics in MedicineWiley

Published: Dec 15, 1994

References

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