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

摘要 酒精使用障碍（AUD），如DSM-5中所定义的，代表了一种高度流行、昂贵且通常未经治疗的 在美国的条件。药物治疗为治疗AUD和改善 这种使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。识别治疗反应者为推进临床护理提供了重要机会 通过基于良好临床应答的变量/预测因素的个性化用药实践来治疗AUD。 例如，虽然纳洛酮等药物的效应量被认为是小到中等，但许多药物的效应量都很小。 过去十年的研究表明，对于某些亚组，其效应量可能大得多 病人。推进AUD的精准医疗，并利用来自大量仔细研究的数据， 针对AUD进行了RCT，本R 03申请旨在进行二次数据分析。我们特别 建议分析NIAAA临床研究小组（NCIG）进行的四项RCT的数据。这些 最先进的AUD RCT已经测试了以下药物疗法：（a）奎替鲁胺，（B）左乙拉西坦 XR（Keppra XR®），（c）伐尼克兰（Chantix®）和（d）HORIZANT®（加巴喷丁Enacarbil）缓释剂。 在这个R 03应用程序中，我们建议使用机器学习方法来识别患者中的治疗应答者。 NCIG RCT。机器学习代表了一种非常有前途但未充分利用的数据分析策略， AUD治疗反应字段。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。这导致模型更普遍， 这与我们在RCT中识别应答者的目标非常吻合。利用这些数据 通过二次数据分析和使用新的分析方法，即机器学习， 为确定AUD药物治疗应答者提供了一种具有成本效益的方法。

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.