Background The prevalence of cannabis use disorder (CUD) has been increasing recently
and is expected to increase further due to the rising trend of cannabis legalization. To help
stem this public health concern, a model is needed that predicts for an adolescent or young
adult cannabis user their personalized risk of developing CUD in adulthood. However, there
exists no such model that is built using nationally representative longitudinal data. Methods
We use a novel Bayesian learning approach and data from Add Health (n= 8712), a …