OBJECTIVES: Oncology trials increasingly permit switching from standard care (SC) to the new treatment following disease progression. Methods to remove the effect of the active treatment in the SC arm are used by HTA agencies to estimate what the effect of the SC would’ve been had crossover not occurred. One method is using RPSFT models to derive counterfactual survival times without crossover. For CE modeling, these counterfactual survival times need to be parametrically extrapolated to estimate life expectancy. It is known that the RPSFT approach introduces additional uncertainty and e.g. the standard error of a hazard ratio calculated from counterfactual survival times needs to be inflated. Traditional methods of parametric survival analysis don’t account for this increased uncertainty which could influence allocation decisions. METHODS: A dataset of 400 patients was simulated assuming a Weibull distribution for PFS and OS with 70% of the patients in the SC arm switching after progression. Life expectancy was calculated in two scenarios. In scenario 1 the RPSFTM adjusted OS had Weibull parameter estimates and covariance calculated directly from the counterfactual survival times. In scenario 2 the data was bootstrapped 1000 times. For each iteration a new RPSFT model, associated counterfactual survival times and Weibull functions were fitted. The mean and covariance of these 1000 parameter estimates was taken. RESULTS: Mean incremental life expectancy after adjusting for cross-over was the same with and without bootstrapping. When PSA was run, larger confidence intervals in the scenario with bootstrapping indicated, the traditional approach failed to account for the increased uncertainty and underestimated the probability of the new treatment being less efficacious (0.4% without compared to 13.4% with bootstrapping). CONCLUSIONS: Failing to appropriately reflect the uncertainty underlying parameter estimates of crossover adjusted survival times could impact HTA decisions when appraisals are based on the likelihood of a treatment being cost effective.