OBJECTIVES: Measurement of the risk-benefit tradeoffs in healthcare decision-making relies on capturing preferences for treatment attributes that are most important to individuals.  The goal of this study is to develop and validate a methodological framework for identifying, validating, and prioritizing attributes for inclusion in discrete choice experiments (DCE).  METHODS: The study enrolled 48 caregivers of a child aged 26 or younger diagnosed with an intellectual disability and mental health disorder.  Data were collected through IDIs (n=6) and six focus groups (n=42).  Following qualitative methods for grounded theory and content analysis, data were analyzed in four distinct steps.  First, in-depth interviews (IDIs) were analyzed to identify concepts reflecting distinct situations influencing treatment decisions.  Second, the concepts were validated by researcher-caregiver agreement in defining the concept.  Third, caregivers prioritized the concepts by selecting those that were most influential in making treatment decisions for their child.  Fourth, a final list of attributes was chosen based on the subset of attributes that had high researcher-caregiver agreement and that were a high priority. Triangulation, member checking, and participants’ and stakeholder partners’ feedback was used throughout the process. RESULTS: Sixteen concepts were identified from the IDIs. Researcher-caregiver agreement in concept definition ranged between 21-79%. The concepts rated as high priorities in decision-making were managing the child’s behavior, advocating for the child’s needs, and communicating with providers.  The financial impact and getting a label were low priorities in treatment decisions.  Seven concepts rated as low priorities and with low definition agreement were discarded. This resulted in a final list of nine attributes. CONCLUSIONS: Systematic methods for attribute identification, as well as stakeholder involvement, will inform the development of DCE instruments that closely reflect risk-benefit tradeoffs in healthcare decisions. Methodological standards for attribute identification would enhance the application and interpretation of DCE in preference elicitation.