MCDA models combine multiple dimensions of value (“attributes”) into a single metric, hence allowing evaluation of healthcare interventions in comprehensive ways using specific decision-makers’ values. Using different approaches, all MCDA models have two common features: (a) elicitation of decision makers’ values, and (b) transforming the performance of candidates (on multiple dimensions of value) into common scales (“data scaling”). This course emphasizes how to accomplish these key steps when groups (vs. individuals) are the decision makers (or are advising a final single decision maker). Various MCDA models differ substantially on the number of decisions required (by decision makers) to complete the models, a complexity that is exacerbated in settings with group decision making (voting), versus individual decision making. This course will review the virtues and complications of different MDCA models and provide hands-on testing of several voting methods to combine individuals’ preference weights into a group weights. Separately, we will explore various data-scaling mechanisms used in multi-criteria models, the errors they might introduce, and examine ways to simplify these processes. Finally, we will explore methods to create decision cut-offs (maximum willingness to pay) in multi-criteria models, akin to those used in cost-effectiveness analysis (CEA) when multiple factors interact under budget constraints. This intermediate-level course presumes at least an introductory familiarity with multi-criteria decision analysis (MCDA) models. It is designed to supplement (rather than as an alternative to) previous ISPOR Short-Courses that discuss MCDA at an introductory level.