OBJECTIVES The term "fuzzy logic" was introduced in 1965 by LAZadeh. Compared to traditional logic, fuzzy logic variables may have a truth value in degree . Fuzzy logic has been applied to many fields, from economic analysis, to artificial intelligence. However it has not been applied so far to pharmacoeconomics. We present a model of pharmacoeconomic decision based on fuzzy logic (Fuzzy Economic Review2001; 6(2): 51-73) and applied to the selection of ranibizumab-aflibercept in treating AMD METHODS According to a decision analysis model based on fuzzy logic 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 ranibizumab and aflibercept were established based on pivotal clinical trials at 52 weeks cited by the EMEA. RESULTS The matrices obtained for ranibizumab was (0.29, 3.55 10^-4, -1.36 10^-4