To investigate heterogeneity in longitudinal patterns of substance use and the associations of use with recovery outcomes across a large sample of patients with different SMIs, our analytic approach focused on examining: (1) longitudinal patterns in the proportion of patients using substances; (2) diagnostic differences in these patterns; (3) the longitudinal associations between substance use over 1-year with symptom and functional recovery; and (4) diagnostic differences in the association between substance use and recovery outcomes. These questions were examined by employing a series of mixed-effects growth models, which is a form of hierarchical linear modeling for repeated measures data, where multiple measurement occasions are nested within individuals [46]. Longitudinal patterns in the proportion of individuals using substances over 1-year were examined using generalized linear mixed-effects growth models employing penalized-quasi likelihood estimation for computing parameter estimates of binary outcomes. These analyses 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
