Participants were classified into different drinking trajectory groups on the basis of their frequency of binge drinking during the follow-up interval, using a discrete mixture modeling approach, with the number of trajectory groups determined by the Bayesian Information Criterion for model selection.80 Linear trend analyses81 were conducted separately for the light drinking and heavy drinking groups to examine the relationship of alcohol response change scores to the drinking trajectory groups. These analyses controlled for the main sociodemographic risk factors associated with alcohol misuse by including them as covariates. Finally, the association of each participant’s alcohol responses to quarterly frequency of subsequent binge drinking was examined using a generalized estimation equations modeling approach82 with alcohol response score, measurement time, and their interaction as covariates. The analysis used a logit link function because of the binomial distribution of binge drinking frequency. Composite generalized estimation equations models were then conducted to examine the effects of each response after controlling for the other significant responses. Given the low frequency of binge drinking among LD, generalized estimation equations analysis using any drinking frequency as the
