Using Classification Trees to Examine Predictors of Marijuana Use Among a Wide Range of Variables

Background: Young adults have elevated risk for negative marijuana use-related outcomes, and there is heterogeneity among users. Identifying risk factors for marijuana user status will improve understanding of different populations of users, which may inform prediction of individuals most likely to experience negative outcomes. Objectives: To identify predictors of marijuana use initiation in young adults. We simultaneously examined a broad range of potential predictors and all their possible interactions, including constructs that have not been previously studied in substance use initiation research. Methods: Data were repeated cross-sectional survey responses from college students in Colorado (N = 4052, 77% White, 61% female, mean age = 22.77). Measures came from the National College Health Assessment, which assesses numerous health and behavioral constructs. We used recursive partitioning and random forest models to identify predictors of ever having used marijuana out of 206 variables. Results: Classification trees identified engagement in increased alcohol use and sexual behavior as salient correlates of marijuana use initiation. Parsimonious recursive partitioning trees explained a substantial amount of variability in marijuana user status (39% in the full model and 24% when alcohol variables were excluded). Random forest models predicted user status with 74.11% and 66.91% accuracy in the full model and when alcohol variables were excluded, respectively. Conclusions: Results support the use of exploratory analyses to explain heterogeneity among marijuana users and non-users. Since engagement in other health-risk behaviors were salient predictors of use initiation, prevention efforts to reduce harm from marijuana use may benefit from targeting risk factors for health-risk behaviors in general.