Predicting treatment cost, life expectancy and quality-adjusted-life-year of different scenarios for Follicular Lymphoma (FL): A discrete event simulation model on a UK population based observational cohort

Abstract: Current economic evaluation studies in oncology usually focus on clinical trial results for specific drugs, which may not always be generalisable to the general population. Furthermore, economic models built around such evaluations can be restricted to specific treatments and patient groups, and are time consuming as need to be rebuilt to adapt to new treatments. Based on a representative population-based patient-cohort, the objective of this study was to develop a generic and flexible economic decision model that could reflect real-world practice, predict cost, survival and quality-adjusted life year (QALY), and test cost-effectiveness of different treatment scenarios at any part of the treatment pathway. Future application of the model could be to support healthcare policy making, especially in the era of personalised medicine.

 

This talk will demonstrate how the generic disease model was built, how it works and how it can be used and applied to decision making in practice. I will also discuss the data needs of such a model. A case study based on the introduction of this novel approach for follicular lymphoma (blood cancer) and related scenario analyses will be presented and discussed. An online tool for supporting decision making will also be presented.