Predictors of Psychedelics, Ketamine, and LSD use among LGBTQ+ youths: A Machine Learning Analysis
Abstract
This is a planned secondary data analysis of cross-sectional data from 1500 Canadian LGBTQ+ youths (aged 16-29) taken from a smoking cessation campaign. We plan to identify the most important predictors that are associated with any use psychedelics, ketamine or LSD in the past 12 months among a set of 96 candidate predictors with machine learning methods such as random forest and LASSO. We also aim to identify interactions between sociodemographic characteristics and the most important predictors of the outcome. Few research had been done in this particular topic, so this will be an exploratory analysis with no prespecified hypothesis. Data requests can be made available upon REB approval
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