Sensitivity analysis (SA) plays a central role in a variety of statistical methodologies, including classification and discrimination,
calibration, comparison and model selection. SA gives a simple model by identifying the importance of
covariates, so a few important covariates will be included in the model based on their contribution in explaining the variation in the data. SA is the study of how the uncertainty in the output of a model (numerical or otherwise) can be apportioned to different sources of uncertainty in the model inputs. SA is hence considered by some as a prerequisite for a model building in any setting, and in any field where models are used. It allows the impact of different factors on response variable to be analyzed. It helps to explain the impact of different model structures. Furthermore, SA can be used to find out which subset of input factors accounts for most of the output variance. SA has been used extensively in linear
regression models, but not in survival regression models. Also SA is an easy and useful method to screening variables in survival regression models. This study presents SA in survival regression models; an application in the medical field is used to illustrate it.