Simulation studies were performed to assess the performances in realistic estimation problems of B-splines (BS) and restricted cubic splines (RCS) within flexible models for survival data. Several theoretical distributions, designed to match the estimates from clinical cancer studies, were adopted for data generation, including a proportional hazards (PH) model and a non-PH one. The simulation plan included sample sizes equal to 500 and 2000, with 10% censored times within the follow-up period. The investigated performances were: estimation error (through MSE), agreement of the estimates with the theoretical target function (through R \(^2\) ), and the ability to identify the correct number of peaks of the target hazard, along with the ability to detect peak times and peak heights within clinically relevant ranges. We found reduced performances in the pattern identification task, even for the simplest distributional models, for which estimation error was optimal. Overall, BS slightly outperformed RCS in pattern identification, while estimation error was equivalent. These results will be compared to further simulations concerning a more extensive set of smoothing methods.