Analyses of time-to-event outcomes almost infallibly rely either on the survival probability at a given time or on the hazard ratio(s) associated with some variable(s) of interest. However, these quantities may be confusing and hard to communicate to the general public. Furthermore, when cancer is the disease of interest most population-based studies focus on the net survival. Net survival is crucial for comparison purposes between populations, but it is less appropriate for planning a health policy or describing a patient’s prognosis, because it is defined in the hypothetical world. Therefore, it is essential that we use alternative survival indicators that could cover these needs, and that could be estimated using population-based data, where the cause of death is usually not available/accurate. Useful alternative indicators that could summarize the survival experience efficiently at both population and individual levels include: the Crude Probability of Death (CPr) and the number of Life years Lost (LYL) detailed by cause of death. These indicators may be expressed using the cause-specific, the subdistribution, and the excess hazard depending on the availability of the cause of death information (ie, either in the cause-specific or the relative survival setting). Their estimation could be achieved with non-parametric methods and regression models. The aim of this PhD is to add to this topic by presenting two new methods for estimating the CPr and the LYL using flexible regression models (in both settings) and the pseudoobservations approach (in the relative survival setting). These methods have the additional advantage of providing covariate effects on the quantities of interest. This thesis includes one paper summarising the alternative indicators, two scientific papers that focus on the new methods, and two R tutorials that show how the new methods may be applied to R software.