Physical Sciences and Mathematics Commons^™

Predictive Modeling Of Adolescent Cannabis Use From Multimodal Data, Philip Spechler

Graduate College Dissertations and Theses

Predicting teenage drug use is key to understanding the etiology of substance abuse. However, classic predictive modeling procedures are prone to overfitting and fail to generalize to independent observations. To mitigate these concerns, cross-validated logistic regression with elastic-net regularization was used to predict cannabis use by age 16 from a large sample of fourteen year olds (N=1,319). High-dimensional data (p = 2,413) including parent and child psychometric data, child structural and functional MRI data, and genetic data (candidate single-nucleotide polymorphisms, "SNPs") collected at age 14 were used to predict the initiation of cannabis use (minimum six occasions) by age 16. …