Survival analysis is characterized by the need to deal with incomplete observation of outcome variables, most frequently caused by right-censoring, and several - now standard - inference procedures have been developed to deal with this. Examples include the Kaplan-Meier estimator for the survival function and partial likelihood for estimating regression coefficients in the proportional hazards (Cox) regression model. During the past 15 years, methods based on pseudo-observations have been studied. Here, the idea is to apply a transformation of the incompletely observed survival data and, thereby, to create a more simple data set on which `standard' techniques (i.e., for complete data) may be applied, e.g., methods using generalized estimating equations (GEE). (Presentation 40 min. + Discussion 20 min.)