Background The treatment of acute myeloid leukemia (AML) is moving towards personalized medicine. However, due to the low incidence of AML, it is not always feasible to evaluate the cost-effectiveness of personalized medicine using clinical trials. Decision analytic models provide an alternative data source. Objective The aim of this study was to develop and validate a decision analytic model that represents the full disease course of AML. Methods We used a micro simulation with discrete event components to incorporate both patient and disease heterogeneity. Input parameters were calculated from patientlevel data. Two hematologists critically evaluated the model to ensure face validity. Internal and external validity was tested by comparing complete remission (CR) rates and survival outcomes of the model with original data, other clinical trials and a population-based study. Results No significant differences in patient and treatment characteristics, CR rate, 5-year overall and diseasefree survival were found between the simulated and original data. External validation showed no significant differences in survival between simulated data and other clinical trials. However, differences existed between the simulated data and a population-based study. Conclusions The model developed in this study is proved to be valid for analysis of an AML population participating in a clinical trial. The generalizability of the model to a broader patient population has not been proven yet. Further research is needed to identify differences between the clinical trial population and other AML patients and to incorporate these differences in the model.

Additional Metadata
Keywords AML, acute myeloid leukemia, decision-making
Persistent URL dx.doi.org/10.1007/s40273-013-0058-1, hdl.handle.net/1765/40451
Journal PharmacoEconomics
Citation
van Dongen-Leunis, A, Redekop, W.K, Montfort, C.A.G.M, Löwenberg, B, & Uyl-de Groot, C.A. (2013). The Development and Validation of a Decision-Analytic Model Representing the Full Disease Course of Acute Myeloid Leukemia. PharmacoEconomics, 31(7), 605–621. doi:10.1007/s40273-013-0058-1