Predicting adolescent problem use of marijuana: development and testing of a Bayesian model.

Abstract

This paper reports on the development and testing of a risk assessment index for problem marijuana use designed to guide teenagers through an extensive computer-based support system intended to help them improve marijuana-related behaviors. Bayesian decision theory, used as the basis of the index development process, offers the advantage of building the index on subjective judgments of experts and does not require a large empirical data base. The index was found to predict an independent panel's ratings of teenager risk, and predict the marijuana use of 10th graders using self-reports of their profiles in the 7th grade. Implications for future risk assessment developments are discussed.