Sample size calculations in the presence of competing risks

Recently, with the growth of statistical developments for competing risks analysis, some methods have been proposed to compute sample size in this context. These methods differ from a modelling approach: one is based on the Cox regression model for the cause-specific hazard, while another relies on the Fine and Gray regression model for the subdistribution hazard of a competing risk. In this work, we compare these approaches, derive a new sample size for comparing cumulative incidence functions when the hazards are not proportional (either cause-specific or subdistribution) and give practical advices to choose the approach best suited for the study question.