Clinical outcome assessments (COAs) provide critical data on a patient’s disease status, including the symptoms of the disease and the impacts of the disease on functioning. In drug development, COAs may be included in the endpoint model to support product differentiation in terms of efficacy or safety for product labeling or medical communications. In order to meet the requirements of such stakeholders, COAs must undergo rigorous qualitative development and psychometric testing to ensure they are fit for purpose within the prespecified context of use. This roundtable will provide a discussion of psychometric methods for evaluating reliability, validity, and interpretation of scores and how these methods might differ from better-known statistical methods. Content will be focused on the definition and relevance of COAs in accordance with current FDA and EMEA regulatory guidance, including evidentiary standards expected by key stakeholders. As part of the evidence required, qualitative evidence and methods used to produce comprehensive and sensitive COAs, including novel methods for integrating exploratory quantitative analyses into qualitative studies (i.e., mixed methods research) will be discussed as the first level of COA development. The roundtable will then address measurement properties that must be considered for any COA and the methods by which these properties can be evaluated. Interpretation of COAs included in clinical studies is paramount for understanding patient outcomes data such as change from baseline to key assessments, time to meaningful change, and sensitivity to change relative to change in clinical endpoints. The discussion leaders will introduce various techniques for modeling and interpreting individual-level change and group-level differences on COAs which includes statistical methodologies that are key to evaluating COA endpoints for cross-sectional and longitudinal efficacy within clinical studies. During this roundtable, participants should be interested in discussing the following topics: 1. How do psychometric methods for instrument development differ from statistical methods for analysis of clinical trial data? 2. What is the value of latent variable modeling and under what conditions should it be used?