prognostic models in survival analysis are aimed at understanding the relationship between patients' covariates and the distribution of survival time. traditionally, semi-parametric models, such as the cox model and the additive hazards model, have been assumed. in this talk i will derive a measure of explained variation under the additive hazards model showing its properties. moreover i will describe the development of a new flexible method for survival prediction: deephazard, a neural network for time-varying risks. i will show its performance on popular real datasets.