This is a competitive revision (i.e., supplement) for R01DA030369 (Computer Based Training in CBT for Spanish-Speaking Substance Users; Principal Investigator: Carroll; 3/15/12 - 2/28/18) submitted in response to NIDA Notice DA-14-049 (Availability of Funds for Competitive Revision Applications for Research to Determine if Reduction in Illicit Drug use is Associated with Improvement in Clinical Outcomes). Progress in the treatment for cocaine use has been negatively impacted by the lack of clinically meaningful and broadly accepted indicators of treatment `success'. Systematic empirical evaluations of outcome indicators for cocaine use trials are rare, in large part due to the lack of specialized datasets required for such purposes. In response to this Notice, we propose to develop a pooled dataset of 720 unique individuals randomized to treatment in one of 7 independent randomized clinical trials targeting cocaine use disorders conducted at Yale between 2002-2014. For all studies, measures of drug use within treatment include daily self-reports of cocaine and other drug use as well as urine toxicology screens collected at least weekly. Follow-up interviews include both extensive self-report and urine toxicology screens collected at 1, 3, 6, and 12 months after the end of treatment with data collected on 80-99% of each treatment cohort. This large and uniquely complete dataset will provide adequate power to apply longitudinal mixture models and other analytic approaches to evaluate relationships between reduction in cocaine use during treatment with improved functional (e.g., health, social, economic), psychosocial, and other health-related outcomes, with the ultimate goal of identifying clinically useful and valid outcome indicators of treatment success. Functional outcomes will include indices of problem severity across multiple life domains (e.g., medical, legal, employment, psychiatric), as well as combined measures of several candidates of potential `clinically meaningful outcomes'. Longitudinal mixture models will be used to take a person-centered approach to examining common patterns of change across participants over multiple time points (i.e., cocaine use trajectories).