began with unconditional growth models predicting substance use from time (coded as 0=baseline; 1=10 weeks, 2=20 weeks, etc.) to examine the overall trajectory of the proportion of the sample using substances throughout the follow-up. Subsequently, conditional growth models were constructed predicting substance use from diagnosis and a diagnosis by time interaction to examine diagnostic differences in longitudinal trajectories of substance use. To examine the association between substance use and functional outcome, general linear mixed-effects models using restricted maximum likelihood estimation were constructed predicting symptom and functioning measures from time and time-varying substance use variables. Diagnostic differences in these relationships were also investigated by examining diagnosis by substance use interactions. Finally, exploratory analyses were conducted to examine the degree to which gender moderated these relationships. All conditional growth models included age, race, and gender, as well as initial levels of the outcome variable that was under study (e.g., baseline substance use/functioning) as potentially confounding covariates. Additionally, level of psychopathology, represented by total BPRS scores, was entered in the alcohol use outcome models due to its observed relation with alcohol use.
