title: 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.
authors.affiliation: Department of Industrial Engineering, University of Wisconsin-Madison.
authors.forename: D H
authors.initials: DH
authors.lastname: Gustafson
authors.forename: K
authors.initials: K
authors.lastname: Bosworth
authors.forename: C
authors.initials: C
authors.lastname: Treece
authors.forename: Y C
authors.initials: YC
authors.lastname: Wu
authors.forename: C G
authors.initials: CG
authors.lastname: Palmer
authors.forename: D P
authors.initials: DP
authors.lastname: Moberg
authors.forename: R P
authors.initials: RP
authors.lastname: Hawkins
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D002648:Child; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017144:Focus Groups; D006801:Humans; D007600:Judgment; D002189:Marijuana Abuse / Q000517:prevention & control* / Q000523:psychology; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales; D012015:Reference Standards; D015203:Reproducibility of Results; D012307:Risk Factors