THE GAP BETWEEN ECONOMIC EVALUATIONS AND CLINICAL PRACTICE: A SYSTEMATIC REVIEW OF ECONOMIC EVALUATIONS ON DABIGATRAN FOR ATRIAL FIBRILLATION
SourceInternational Journal of Technology Assessment in Health Care, 34, 3, (2018), pp. 327-336
Article / Letter to editor
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International Journal of Technology Assessment in Health Care
SubjectRadboudumc 13: Stress-related disorders DCMN: Donders Center for Medical Neuroscience; Radboudumc 15: Urological cancers RIHS: Radboud Institute for Health Sciences; Radboudumc 16: Vascular damage RIHS: Radboud Institute for Health Sciences
OBJECTIVES: As model-based economic evaluations (MBEEs) are widely used to make decisions in the context of policy, it is imperative that they represent clinical practice. Here, we assess the relevance of MBEEs on dabigatran for the prevention of stroke in patients with atrial fibrillation (AF). METHODS: We performed a systematic review on the basis of a developed questionnaire, tailored to oral anticoagulation in patients with AF. Included studies had a full body text in English, compared dabigatran with a vitamin K antagonist, were not dedicated to one or more subgroup(s), and yielded an incremental cost-effectiveness ratio. The relevance of all MBEEs was assessed on the basis of ten context-independent factors, which encompassed clinical outcomes and treatment duration. The MBEEs performed for the United States were assessed on the basis of seventeen context-dependent factors, which were related to the country's target population and clinical environment. RESULTS: The search yielded twenty-nine MBEEs, of which six were performed for the United States. On average, 54 percent of the context-independent factors were included per study, and 37 percent of the seventeen context-dependent factors in the U.S. STUDIES: The share of relevant factors per study did not increase over time. CONCLUSIONS: MBEEs on dabigatran leave out several relevant factors, limiting their usefulness to decision makers. We strongly urge health economic researchers to improve the relevance of their MBEEs by including context-independent relevance factors, and modeling context-dependent factors befitting the decision context concerned.
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