Baseline and 6-year follow-up background and drinking characteristics were compared by dependent sample Student t tests. Generalized equation estimation (GEE) models, which are similar to generalized linear mixed models for analyzing longitudinal data but are more robust in specifying variance-covariance structures using a sandwich algorithm, were used to examine alcohol responses and future drinking outcomes. The primary alcohol response variables were B-BAES derived B-STIM and B-SED to test directly low-level response and differentiator models. Secondary measures were DEQ derived “like” and “want more” to test the incentive sensitization theory. The primary outcome was the mean number of AUD symptoms met during follow-up, analyzed using GEE with a log link function for count data. The GEE models included standardized alcohol response (stimulation, sedation, wanting, liking), follow-up time, and their interaction. Whereas age, sex, race, education, and disinhibited personality (38) were not associated with AUD symptom count, FH was significantly associated (positive vs. negative: β [SE] = .327 [.125], p = .009); therefore, GEE analyses controlled for FH by including two dummy variables (FH positive vs. negative, FH unsure vs. negative). The
