Abstract
This paper studies influence diagnostics (Cook's Distance and Likelihood Distance) on competing risk models when true covariates are observable. The one-step EM and one-step ML methods are used to detect influential observations for Cox's model. It was found that results generated from data analysis using one-step EM algorithm are better than results obtained by using one-step ML method. Moreover, Cook's distance show better results compare to likelihood distance.