Moderators and Predictors of Response to Treatments for Alcohol Dependence

酒精依赖治疗反应的调节因素和预测因素

• 批准号: 8293908
• 负责人: Ralitza Gueorguieva
• 金额: $ 21.29万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8293908
• 关键词: Abstinence; Address; Alcohol dependence; Behavior Therapy; Behavioral; Characteristics; Classification; Clinical; Data; Decision Trees; Effect Modifiers (Epidemiology); Effectiveness; Enrollment; Equation; European; Evaluation; Funding; Goals; Grant; Heterogeneity; Intervention; Knowledge; Literature; Medication Management; Methods; Naltrexone; Outcome; Patients; Pattern; Pharmaceutical Preparations; Placebos; Population; Randomized; Randomized Clinical Trials; Sampling; Statistical Methods; Subgroup; Testing; Therapeutic; Time; Treatment Effectiveness; Treatment outcome; Trees; Validation; Work; acamprosate; alcohol research; base; clinical decision-making; clinical practice; design; drinking; efficacy testing; endophenotype; follow-up; forest; insight; novel; response; treatment effect

DESCRIPTION (provided by applicant): The primary objective of randomized clinical trials is to assess average treatment effects. However, due to between-subject heterogeneity, treatments may work only in a subset of the population and may not work or may be even counter-therapeutic in another subset. For such treatments, average effect may be small to non- existent and hence these treatments may be underutilized in a population for whom they might provide significant benefit. Since alcohol dependence is characterized by high patient heterogeneity and treatment effects have been shown to be in the small to medium range, it is crucial to identify specific covariates (moderators) that stratify the population into subgroups fo which treatments have differential effects. The usual approach has been to consider baseline predictors one at a time or to test treatment effects among predefined endophenotypes. This limits the potential for discovery of important combinations of predictor variables that might moderate treatment effects. In this application we propose to apply tree-based and forest-based methods that address the limitations of considering predictors one at a time. Decision trees allow identification of subgroups of subjects for whom there are significant differences in effectiveness of treatments. They can consider a large pool of predictor variables and empirically derive the strongest predictors/moderators that identify subgroups of patients with differential treatment effects. Compared to classical classification methods, tree-based methods may be easier to use in clinical settings because they require evaluation of simple decision rules rather than mathematical equations. We will apply the methods to the U.S.-based COMBINE study that evaluated efficacy of naltrexone, acamprosate and CBI; consider the results in the context of the subject-matter literature; and based on the results for COMBINE formulate and test hypotheses using classical statistical methods on the European PREDICT study that was designed to be comparable to COMBINE. Our goal is to identify robust predictors and moderators of treatment effects during treatment (Specific aims 1 and 2 respectively) and during follow-up (Specific aim 3) and to inform clinicians making treatment decisions in different subgroups of alcohol- dependent patients using simple to interpret and externally validated decision rules. PUBLIC HEALTH RELEVANCE: This project will provide easily interpretable and bust decision rules identifying subgroups of patients with good outcome irrespective of the type of treatment and subgroups for which specific treatments or treatment combinations may be most effective. The rules will be developed using a sophisticated and powerful approach called tree-based methods on data from the COMBINE Study that tested the efficacy of naltrexone, acamprosate and a specialized behavioral intervention for alcohol dependence. The results will be externally validated on the PREDICT study that tested the efficacy of naltrexone, acamprosate and CBI as an augmentation strategy in a different population. The ultimate goal of this approach is to inform clinicians making treatment decisions for different subgroups of alcohol-dependent patients and to present the decision rules in simple form that can be widely applied in clinical practice.

描述（由申请人提供）：随机临床试验的主要目的是评估平均治疗效果。然而，由于受试者之间的异质性，治疗可能仅在一部分人群中有效，而在另一部分人群中可能不起作用，甚至可能起到反治疗作用。对于此类治疗，平均效果可能很小甚至不存在，因此这些治疗可能在它们可能提供显着益处的人群中未被充分利用。由于酒精依赖的特点是患者异质性高，并且治疗效果已显示在小到中等范围内，因此确定特定的协变量（调节因子）至关重要，该协变量将人群分层为治疗效果不同的亚组。通常的方法是一次考虑一个基线预测因子，或者测试预定义的内表型中的治疗效果。这限制了发现可能调节治疗效果的重要预测变量组合的潜力。在此应用中，我们建议应用基于树和基于森林的方法来解决一次考虑一个预测变量的局限性。决策树可以识别治疗效果存在显着差异的受试者亚组。他们可以考虑大量的预测变量，并凭经验得出最强的预测变量/调节变量，以识别具有差异治疗效果的患者亚组。与经典分类方法相比，基于树的方法可能更容易在临床环境中使用，因为它们需要评估简单的决策规则而不是数学方程。我们将把这些方法应用到美国的 COMBINE 研究中，该研究评估了纳曲酮、阿坎酸和 CBI 的疗效；在主题文献的背景下考虑结果；并根据 COMBINE 的结果，使用欧洲 PREDICT 研究的经典统计方法制定和检验假设，该研究旨在与 COMBINE 进行比较。我们的目标是确定治疗期间（分别为具体目标 1 和 2）和随访期间（具体目标 3）治疗效果的稳健预测因素和调节因素，并告知临床医生使用简单的方法对酒精依赖患者的不同亚组做出治疗决策。解释并经过外部验证的决策规则。 公共卫生相关性：该项目将提供易于解释和全面的决策规则，以确定具有良好结果的患者亚组，无论治疗类型如何，以及特定治疗或治疗组合可能最有效的亚组。这些规则将使用一种复杂而强大的方法（称为基于树的方法）根据 COMBINE 研究的数据制定，该研究测试了纳曲酮、阿坎酸和针对酒精依赖的专门行为干预的功效。结果将在 PREDICT 研究中得到外部验证，该研究测试了纳曲酮、阿坎酸和 CBI 作为增强策略在不同人群中的功效。这种方法的最终目标是告知临床医生针对不同亚组的酒精依赖患者做出治疗决策，并以简单的形式呈现可广泛应用于临床实践的决策规则。