Identifying treatment responders in medication trials for AUD using machine learning approaches

摘要 根据DSM-5的定义，酒精使用障碍(AUD)代表着一种非常普遍、代价高昂且通常未得到治疗的疾病 美国的情况。药物治疗为AUD的治疗和改善提供了一条很有希望的途径 这种令人衰弱的疾病的临床结果。虽然开发治疗AUD的新药仍然很高 优先研究领域，仍有重大机会进一步阐明已完成的临床反应 药物试验。为此，随机临床试验(RCT)中的一个关键问题是哪些患者对 给予药物治疗。识别治疗应答者为推进临床护理提供了重大机遇 通过根据良好临床反应的变量/预测因素个人化用药实践，对AUD进行治疗。 例如，尽管纳曲酮等药物的效应量被认为是小到中等，但许多 过去十年的研究表明，它对某些亚群的影响规模可能要大得多。 病人的数量。朝着推进AUD的精准医学和利用来自大量谨慎的 为澳元进行随机对照试验，此R03应用程序寻求进行二次数据分析。具体来说，我们 建议分析NIAAA临床研究小组(NCIG)进行的四项随机对照试验的数据。这些 治疗AUD的最先进的随机对照试验已经测试了以下药物疗法：(A)奎硫平，(B)左乙拉西坦 XR(Keppra XR®)、(C)Varenicline(Chantix®)和(D)Horizant®(加巴喷丁曲卡比)缓释片。 在这个R03应用程序中，我们建议使用机器学习方法来识别 NCIG RCT。机器学习代表了一种非常有前途且未得到充分利用的数据分析策略 AUD治疗应答场。机器学习模型优先考虑预测未来结果的能力 为手头的数据创建完美的拟合模型。这导致模型更容易推广到 未来的观察，这与我们在随机对照试验中确定应答者的目标很好地吻合。利用这些数据库中的数据 通过二次数据分析和使用新的分析方法，即机器学习， 提供了一种识别AUD药物治疗应答者的经济有效的方法。

ABSTRACT Alcohol use disorder (AUD), as defined in DSM-5, represents a highly prevalent, costly, and often untreated condition in the United States. Pharmacotherapy offers a promising avenue for treating AUD and for improving clinical outcomes for this debilitating disorder. While developing novel medications to treat AUD remains a high priority research area, there remain major opportunities to further elucidate clinical response in completed medication trials. To that end, a key question in randomized clinical trials (RCTs) is which patients respond to a given pharmacotherapy. Identifying treatment responders provides major opportunities to advance clinical care for AUD by personalizing medication practices on the bases of variables/predictors of good clinical response. For example, while the effect size for medications such as naltrexone is deemed small-to-moderate, a host of studies over the past decade have shown that its effect size may be considerably larger for certain subgroups of patients. Towards advancing precision medicine for AUD and leveraging data from a host of carefully conducted RCTs for AUD, this R03 application seeks to conduct secondary data analysis. Specifically, we propose to analyze data from four RCTs conducted by the NIAAA Clinical Investigations Group (NCIG). These state-of-the-art RCTs for AUD have tested the following pharmacotherapies: (a) quetiapine, (b) Levetiracetam XR (Keppra XR®), (c) Varenicline (Chantix®), and (d) HORIZANT® (Gabapentin Enacarbil) Extended-Release. In this R03 application, we propose to use a machine learning approach to identify treatment responders in the NCIG RCTs. Machine learning represents a highly promising and underutilized data analytic strategy in the field of AUD treatment response. Machine learning models prioritize the ability to predict future outcomes over creating perfectly fitting models for the data at hand. This results in models which are more generalizable to future observations, which fits well with our goal of identifying responders in RCTs. Leveraging data from these pivotal RCTs through secondary data analysis and using novel analytic methods, namely machine learning, provides a cost-effective approach to identifying AUD pharmacotherapy responders.