Advishe: a New Tool to Report Validation of Health-Economic Decision Models.

Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. half-cycle correction method provided more accurate results than calculations without any kind of half-cycle correction with the exception of one set of input parameters. ConClusions: Based on our model the most accurate method for half-cycle correction is Simpson's method as in most cases it was the closest to real data. It is important to note that with a few exceptions even the standard method's results were more accurate than in cases where no half-cycle correction was applied. objeCtives: We present a model decision based on fuzzy logic, and apply to off label use of antiTNF in Crohn's disease (CD) (Infliximab (IFB) 10 mg/kg/8 weeks, adalimumab (ADA) 80mg/2 weeks,. Certolizumab (CZB) 200mg/2weeks). The term " fuzzy logic " (FL) was introduced in 1965 by LAZadeh. Compared to traditional logic, FL variables may have a truth value in degree. FL has been applied to many fields, from economic analysis, to artificial intelligence. However it has not been applied so far to pharmacoeconomics. Methods: According to a decision analysis model based on FL four fuzzy variables that affect the choice of treatment are defined: treatment success (expressed as a probability), cost of success, cost of failure (expressed as inverses), and other conditions about the cost (negotiation, handling of drugs...). Based on the value of these fuzzy variables, three linguistic variables (High, Medium, Low) are defined to expressing convenience of choice. The combination of the three possible values for each of the variables gives us 81 possible decision rules, so that the (HHHH) would be the most favorable option and (LLLL) the more unfavorable. So a new fuzzy variable called " ranking " is established for classifying these options with 7 possible values (Very-unfavorable, unfavorable, slightly-unfavorable, neutral, slightly-favorable, favorable, very-favorable). The value of the fuzzy variables for antiTNF at 52 weeks of treatment, were established based …