The Development and Validation of a Decision-Analytic Model Representing the Full Disease Course of Acute Myeloid Leukemia

• Published in: PharmacoEconomics 2013-07, Vol.31 (7), p.605-621
• Main Authors: Leunis, Annemieke, Redekop, W. Ken, van Montfort, Kees A. G. M., Löwenberg, Bob, Uyl-de Groot, Carin A.
• Format: Article
• Language: English
• Subjects: Adult; Antineoplastic agents; Biological and medical sciences; Blood; Bone marrow; Breast cancer; Cancer therapies; Chemotherapy; Clinical trials; Clinical Trials as Topic; Computer Simulation; Cost-Benefit Analysis; Decision making models; Decision Support Techniques; Disease Progression; Disease-Free Survival; Drug dosages; Estrogens; Female; General aspects; Genetic testing; Health Administration; Health Economics; Health technology assessment; Hematologic and hematopoietic diseases; Humans; Leukemia; Leukemia, Myeloid, Acute - diagnosis; Leukemia, Myeloid, Acute - drug therapy; Leukemias. Malignant lymphomas. Malignant reticulosis. Myelofibrosis; Male; Medical prognosis; Medical sciences; Medicine; Medicine & Public Health; Models, Biological; Original Research Article; Patients; Pharmacoeconomics and Health Outcomes; Pharmacology. Drug treatments; Population-based studies; Precision Medicine; Prognosis; Public Health; Quality of Life Research; Remission (Medicine); Remission Induction; Studies
• ISSN: 1170-7690; 1179-2027
• DOI: 10.1007/s40273-013-0058-1

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 patient-level 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 disease-free 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.