USING THE ANALYTIC HIERARCHY PROCESS TO FILLING MISSING GAPS IN EARLY HEALTH ECONOMIC MODELING Marjan Hummel*, Lotte Steuten, Karin Groothuis-Oudshoorn, Maarten IJzerman Department of Health Technology & Services Research University of Twente, Enschede, The Netherlands E-mail: j.m.hummel@utwente.nl l.m.g.steuten@utwente.nl c.g.m.oudshoorn@utwente.nl m.j.ijzerman@utwente.nl ABSTRACT Background: Health economic modeling is a commonly used approach to support decision making about the adoption and reimbursement of health care technologies. Yet, early medical technology assessment of new technologies is commonly characterized by a lack of data that can be used to populate such models. Purposes: Combining the versatility of the Analytic Hierarchy Process (AHP) with the decision- analytic sophistication of health economic modeling for early health technology assessment in order to fill missing gaps in early models, including patient preferences beyond clinical effectiveness. As an illustration, we apply this methodology to compare the cost-effectiveness of a new technology for diagnosing breast cancer against the usual diagnostic approach. Method: The AHP is a technique for multi-criteria analysis, relatively new in the field of technology assessment, which allows a valid and rapid elicitation and quantification of priors. An AHP has been carried out to compare a new diagnostic method for breast cancer against current practice and the resulting AHP data are converted for use in a simple Markov model to compare the incremental cost-effectiveness of the alternative strategies. Result: We systematically estimated priors on the clinical effectiveness of the new technology. In our illustration, estimations on the sensitivity and specificity of the new diagnostic technology were used as inputs in the Markov model. Moreover, weighted outcome measures including the clinical effectiveness (weight = 0.61), patient comfort (weight = 0.09) and safety (weight = 0.30) could be integrated into one combined outcome measure in the Markov model. Conclusion: Combining AHP and Markov modeling is particularly valuable in early technology assessment when evidence about the effectiveness of health care technology is still missing. Moreover, this combination can be valuable in case decision makers are interested in other patient relevant outcomes measures beyond the technology’s clinical effectiveness, which may not yet be (adequately) captured in the available utility measures.