Bias evaluation in the proportional hazards model

Abstract

We consider two approaches for bias evaluation and reduction in the proportional hazards model proposed by Cox. The first one is an analytical approach in which we derive the n^-1 bias term of the maximum partial likelihood estimator. The second approach consists of resampling methods, namely the jackknife and the bootstrap. We compare all methods through a comprehensive set of Monte Carlo simulations. The results suggest that bias-corrected estimators have better finite-sample performance than the standard maximum partial likelihood estimator. There is some evidence oithe bootstrap-correction superiority over the jackknife-correction but its performance is similar to the analytical estimator. Finaily an application iliustrates the proposed approaches.

Keywords:

• Bias correction
• bootstrap
• jackknife
• Weibull regression model

^*Corresponding author. [email protected].

^*Corresponding author. [email protected].

Notes

^*Corresponding author. [email protected].