In this paper, resampling methods, namely the jackknife and the bootstrap, are considered for bias evaluation and correction of maximum partial likelihood estimators. A complete set of Monte Carlo simulations compare the proposed approaches with formulae recently proposed for bias correction to order n^-1. The results indicate a competitive performance for these methods.