More and more potentially curative interventions become available in oncology. Thus, a growing proportion of cancer patients might indeed be cured of their disease. For reimbursement decisions, it is common practice to estimate long-term overall survival based on trial data and for instance parametric survival curves. These standard parametric methods, however, do not explicitly take
Payers are more interested in effectiveness rather than efficacy of novel treatments. In light of health economics, cost-effectiveness is preferred over cost-efficacy. We aim to assess the use of real-world effectiveness evidence along with efficacy data reported in a clinical trial setting, to estimate effectiveness in a real-world setting with the example of overall survival
Due to immaturity of survival data in clinical trials at time of health technology assessment, long-term predictions of survival result in uncertainty in decision-making. To predict survival more accurately, we test Bayesian methods using historical data to inform parametric survival predictions based on immature trial data.