Bias Evaluation in Theproportional Hazards Modele

We consider two approaches for bias evaluation and reduction in the proportional hazards model (PHM) proposed by Cox. The rst 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 for the special one parameter PHM. The results suggest that bias-corrected estimators have better nite-sample performance than the standard maximum partial likelihood estimator and there is some evidence of the bootstrap-correction superiority. Finally an application illustrates the proposed approaches.