DESCRIPTION: Conjoint analysis are a class of stated-preference survey methods that can be used to elicit responses that reveal preferences, priorities, and importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete-choice experiments (DCE), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other healthcare stakeholders. The “Methods for the Statistical Analysis of Discrete-Choice Experiments” is the third report of the ISPOR Conjoint Analysis Good Research Practices Task Force. The forum will present: 1) a primer on relevant statistical methods including multinomial/conditional logit, random-parameters logit, hierarchical Bayes, and latent-class analysis; 2) a discussion of good research practices for statistical analysis (including the ESTIMATE checklist) and 3) case studies on how these methods can be applied to discrete-choice experiments and other stated-preference methods. The discussion will assist researchers in evaluating and selecting from among alternative approaches to conducting statistical analysis of DCE data. Discussion and audience interactions will be led by Dr. IJzerman, and Drs. Gonzalez, Marshall, and Bridges will present the report and case studies. Attendees will be encouraged to share their experience and perspectives as well as participate in the planned exercises and survey. This interactive and informative workshop will be valuable to researchers, clinicians, and industry analysts who are interested in understanding good research practices for analyzing data from discrete-choice experiments and learning more about the application of stated-preference methods.