Predicting Survival in cost-effectiveness analyses based on clinical trials

This paper deals with the question how to model health effects after the cessation of a randomised controlled trial (RCT). Using clinical trial data on severe congestive heart failure patients we illustrate how survival beyond the cessation of a RCT can be predicted based on parametric survival models. In the analysis we compare the predicted survival and the resulting incremental cost-effectiveness ratio (ICER) of different survival models with the actual survival/ICER. Our main finding is that the results are highly sensitive to the choice of survival model and that extensive sensitivity analysis in the CE analysis is required. We also show that adding the true survival after the end of the clinical study will underestimate the true variability.