Competing risks data usually arises in studies in which the failure of an individual may be classified into one of k (k > 1) mutually exclusive causes of failure. When competing risks are present, classical survival analysis techniques may not be appropriate to use. The main goal of this paper is to review the specific methods to deal with competing risks. To this aim, we first focus on how to specify a competing risks model, which is the structure of observed data in this framework, and how components of the model are estimated from a given random sample. In addition, we discuss how to correctly interpret probabilities in the presence of competing risks, and regression models are considered in detail. To conclude, we illustrate the problem with data from a bladder cancer study.