用于预测大麻使用障碍风险的贝叶斯学习模型的验证
Validation of a Bayesian Learning Model to Predict the Risk for Cannabis Use Disorder
AI总结：
研究在MLS和CHDS两个纵向队列中外部验证基于贝叶斯逻辑回归的Add Health CUD风险预测模型，显示其区分度与校准良好，可用于识别高风险青年大麻使用者。
摘要：
背景：大麻使用障碍（CUD）是一个日益严重的公共卫生问题。及早识别未来有发生CUD风险的青少年和年轻成人，可能有助于遏制这一趋势。近期，研究者开发了一个采用贝叶斯学习方法拟合的逻辑回归模型，基于青少年和青年期的七项风险因素预测未来CUD风险。该模型使用一个具有全国代表性的纵向数据集Add Health进行训练（以下称为Add Health模型）。方法：我们在两个队列上验证了Add Health模型，即Michigan Longitudinal Study（MLS）和Christchurch Health and Development Study（CHDS），使用参与者至约30岁前的纵向数据（以与Add Health的训练数据保持一致）。如果参与者在此期间任何年龄被诊断为CUD，即被视为病例。我们计算了曲线下面积（AUC）以及预期病例数与观察病例数之比（E/O）。我们还探索了对模型进行重新校准，以解释人群患病率差异。结果：用于验证的队列规模为MLS 424人（53例）和CHDS 637人（105例）。两个队列的AUC分别为0.66（MLS）和0.73（CHDS），相应的E/O比值（重新校准后）为0.995和0.999。结论：Add Health模型在两个不同队列上的外部验证，为该模型识别日后发生CUD高风险的青少年或年轻成人大麻使用者的能力提供了信心。
英文摘要：
Background: Cannabis use disorder (CUD) is a growing public health problem. Early identification of adolescents and young adults at risk of developing CUD in the future may help stem this trend. A logistic regression model fitted using a Bayesian learning approach was developed recently to predict the risk of future CUD based on seven risk factors in adolescence and youth. A nationally representative longitudinal dataset, Add Health was used to train the model (henceforth referred as Add Health model). Methods: We validated the Add Health model on two cohorts, namely, Michigan Longitudinal Study (MLS) and Christchurch Health and Development Study (CHDS) using longitudinal data from participants until they were approximately 30 years old (to be consistent with the training data from Add Health). If a participant was diagnosed with CUD at any age during this period, they were considered a case. We calculated the area under the curve (AUC) and the ratio of expected and observed number of cases (E/O). We also explored re-calibrating the model to account for differences in population prevalence. Results: The cohort sizes used for validation were 424 (53 cases) for MLS and 637 (105 cases) for CHDS. AUCs for the two cohorts were 0.66 (MLS) and 0.73 (CHDS) and the corresponding E/O ratios (after recalibration) were 0.995 and 0.999. Conclusion: The external validation of the Add Health model on two different cohorts lends confidence to the model's ability to identify adolescent or young adult cannabis users at high risk of developing CUD in later life.
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