Value of Information (VOI) techniques provide the analytic framework to estimate the value of acquiring additional evidence to inform a decision problem. VOI analysis is increasingly used to inform research prioritization decisions as it allows evaluating the extent to which new evidence might improve expected benefits by reducing the level of uncertainty in the current evidence base and compares that improvement with the cost of conducting the research. The course will provide a thorough understanding of the concepts and methods used in VOI analysis for participants with a working-knowledge of model-based cost-effectiveness analysis and its role in healthcare decision making. The course is based on the recommendations of the ISPOR Value of Information Task Force Reports and will 1) provide participants with an introduction to the fundamentals of VOI; 2) explain why VOI is important to decision makers; 3) identify the types of healthcare decisions that can be supported by VOI, as well as its limitations; 4) describe how the methods should be used and how the results should be interpreted, and 5) explain how VOI can support decision making in different contexts. Participants are provided with the opportunity to engage with concrete applications of the six-step approach for VOI analyses, including identification and characterization of different sources of uncertainty, calculation of the Expected Value of Perfect Information (EVPI), Expected Value of Perfect Parameter Information (EVPPI), Expected Value of Sample Information (EVSI) and Expected Net Benefit of Sampling (ENBS) and interpretation / presentation of results. To help participants engage with the course content, the instructors will walk through each step of the VOI analyses using worked examples (e.g. on VOI of next-gen sequencing studies, immunotherapy trials, and other emerging technologies). They will guide participants through hands-on exercises in Excel and R, show available online VOI tools, and lead discussions on the topic. This course is designed for beginners in VOI analysis that have some experience with probabilistic decision modelling.