The article, “Prediction Rules Identify Which Young Adults Have Higher Rates of Heavy Episodic Drinking After Exposure to 12-Week Text Message Interventions,” has been published in SAj.
In this commentary, the authors state that an alcohol text message intervention recently demonstrated effects in reducing heavy episodic drinking (HED) days at the three month follow-up in young adults with a history of hazardous drinking. An important next step in understanding intervention effects involves identifying baseline participant characteristics that predict who will benefit from intervention exposure to support clinical decision-making and guide further intervention development. To identify baseline characteristics that predict HED, this exploratory study used a prediction rule ensemble (PRE). Compared to more complex decision-tree methods (e.g., random forest), PREs have comparable performance, while generating simpler rules that can directly identify subgroups that do or do not respond to intervention.
In the AUTHORS’ OWN WORDS, they relate the importance of their work:
“Building on prior analyses demonstrating the effectiveness of alcohol text message interventions versus control condition in reducing HED at three month follow-up, this exploratory study examined baseline participant characteristics that predicted HED at three months using PRE. To our knowledge, this is the first use of PRE to identify prediction rules for a digital alcohol intervention. Identifying prediction rules can increase understanding of “for whom” the intervention has effects, and indicate features important to predicting the main intervention outcome. The PRE model included treatment assignment as a confirmatory rule known to predict outcome, and generated information on the baseline characteristics most important to predicting HED at three months to aid model interpretation. This data-driven approach maximized predictive accuracy while minimizing complex rule structure, to generate a set of rules that could be readily understood by clinicians to support young adult HED intervention.”
“This exploratory study identified 12 rules based on a small set of six baseline variables and intervention status (seven total features) that predict HED days at three month follow-up. Future possible clinical application might involve computerized assessment of key baseline predictors of intervention effects on HED days, which include drinking pattern, negative urgency, perceived number of friends who get drunk weekly, and perceived risk of possible HED-related consequences. The rules balance prediction performance and ease of interpretation to estimate short-term HED outcome among young adults exposed to a text message intervention.”