We present various measures, speciﬁcally the expected life-years list due to a cause of death, that can be predicted for a speciﬁc covariate pattern to facilitate interpretation in observational studies. These can also be summarised at the population-level using standardisation to obtain marginal measures. The restricted mean survival time (RMST) measure can be obtained in the presence of competing risks using Royston-Parmar ﬂexible parametric survival models (FPMs). Royston-Parmar FPMs can be ﬁtted on either the cause-speciﬁc hazards or cumulative incidence scale in the presence of competing risks. An advantage of modelling within this framework for competing risks data is the ease at which other alternative predictions to the (cause-speciﬁc or subdistribution) hazard ratio can be obtained. The RMST estimate is one such measure. This has an attractive interpretation, especially when the proportionality assumption is violated. In addition to this, compared to similar measures, fewer assumptions are required and it does not require extrapolation. Furthermore, one can easily obtain the expected number of life-years lost, or gained, due to a particular cause of death, which is a further useful prognostic measure. We describe estimation of RMST after ﬁtting a FPM on either the log-cumulative subdistribution, or cause-speciﬁc hazards scale. As an illustration of reporting such measures to facilitate interpretation of a competing risks analysis, models are ﬁtted to English colorectal data.