Finally, we estimated all covariates simultaneously to determine each individual covariate effect on AUD occurrence across adulthood after adjusting for all other covariates. Figure 2 illustrates the final multivariate model estimated in Mplus, with the binary time-specific event indicators each regressed on T1 age to allow for time-varying effects, and the latent hazard function (estimated from the seven binary time-specific event indicators) regressed on the set of covariates assumed to be proportional across time. Note that the discrete-time survival model is specified as a single-class latent class analyses (represented by the constant latent class “c” variable) in order to obtain the logistic estimates of interest within the SEM framework (Muthen & Masyn, 2005). The results of this model are found in Table 2 under the Full Model heading. After adjusting for all other covariates, the LR to alcohol and Time 1 typical quantity were still significant predictors of the AUD occurrence in adulthood, while the effects of family history (b=.39, p=.076) and age of onset (b=−.09, p=.058) were reduced to marginal significance. Body mass index remained non-significant and the effects
