OBJECTIVES: Regulators of medical technologies are facing increasing pressure to make their deliberations concerning the benefits and risk more transparent. Both benefits and risks are often measured via multiple competing outcomes. Hence, MCDA models like the Analytic Hierarchy/Network Process are valuable tools in quantifying decision trade-offs. The objective of this paper is to demonstrate the use of MCDA models for benefit-risk assessment and the use of sensitivity analysis to assess the impact of uncertainty and patient heterogeneity.   METHODS: Using an existing data set about anti-depressants we construct a decision model for use with AHP weights, including clinical endpoints, adverse events and quality of life. AHP priorities for the main criteria (benefits and risks) were obtained from the general public (n=15) using face–to-face interview. After base-case analysis of decision trade-offs, three forms of sensitivity analysis for MCDA models were employed.  RESULTS: We applied three forms of sensitivity analysis, including 1) manual adjustment of criteria weights using a slider; 2) probabilistic sensitivity analysis (PSA) of the criteria weights; and 3) PSA of the expected drug performance on each of the criteria.  Examples will be graphically presented and discussed.  CONCLUSIONS: One of the advantages of AHP/ANP is its ease of use. However, in order to make judgments about benefits and risks decision makers, may wish to generalize to a wider population and as well as to quantify decision trade-offs in subgroups of patients. The methods employed provide this flexibility. However, the strategy chosen should not be more complex than necessary to support the decision maker.