OBJECTIVES:  To evaluate the gain in power and bias between conventional methods (ITT) and the rank-preserving structural failure time model (RPSFTM) in the presence of treatment switching. METHODS:  The RPSFTM is used for Health Technology Assessment submissions to adjust for switching of patients from reference to investigational treatment in cancer trials. It uses counterfactual survival (survival when only reference treatment would have been used) and assumes that, at randomization, the counterfactual survival distribution for the investigational and reference arm are identical. The RPSFTM is rank preserving, so that the p-value for this method is equal to the ITT p-value. Illness-death models with pre-progression, progression and death and exponential transitions were used to sample patients’ survival times, assuming different amounts of switching and different rates of pre- and post-progression survival. Recruitment is modelled as well. For different maturities, the power of ITT and RPSFTM was evaluated. RESULTS:  For high percentages of treatment switching, almost no gain in power was measured from 30% maturity to 70% maturity, while being as low as 10% for some of the simulations. The gain in unbiasedness from ITT to RPSFTM is lost by having larger uncertainty. CONCLUSIONS: When large amount of treatment switching are expected, sample sizes need to be unreasonably enlarged compared to no treatment switching when using ITT or RPSFTM to obtain reasonable power.