Treatment Effect Heterogeneity in Psychosocial Treatments for Substance Use Disorders

项目摘要 本申请的目的是响应PAR-19-368“加快药物滥用的步伐”提交的 利用现有数据的研究”是利用来自NIDA临床试验网络的数据， 理解物质使用障碍的心理社会治疗的治疗效果异质性。 治疗效果异质性是物质使用障碍治疗研究中特别关注的问题， 部分原因是患者在症状特征、病程和恢复方面的亚表型异质性 弹道然而，物质使用障碍研究中治疗效果异质性的分析 通常使用子组分析以次优方式进行（即，分别估计影响 在由单个协变量定义的组内），这可能导致发现治疗中的虚假差异 由于多个统计检验的性能和患者间的随机变异性，亚组的影响。 基于单协变量的亚组分析的另一个挑战是，大多数协变量的调节作用很小， 效应及其对治疗效应异质性的个体贡献对于 治疗决定。我们的目标是应用一种新的统计方法，因果森林方法， 系统地研究了社会心理治疗对物质使用障碍的治疗效果异质性。 这项研究使用了NIDA临床试验网络中12项随机对照试验的数据， 九种不同心理社会治疗对常规治疗条件的有效性（动机 激励，动机增强疗法，筛查性动机评估，治疗性教育 系统，简易策略家庭治疗，跨步骤促通，动机访谈，寻求安全， 锻炼计划）。对于每一种类型的心理社会治疗，我们建议实施因果森林 一种估计治疗对每个个体的预期效果的方法，同时考虑多个 协变量同时。估计的治疗效果将用于检测以下情况的存在和程度： 治疗效果的异质性在每种类型的心理治疗。使用变量重要性度量 从因果森林中获得，我们还计划确定有助于治疗的最重要的协变量 在每种类型的心理社会治疗中影响异质性。这些分析将重复进行， 结果（例如，禁欲，减少目标物质使用频率），以检查是否以及如何 治疗效果异质性的程度以及共同效果调节因子在结果中不同。总的来说， 这些分析将增加治疗提供者可用于指导治疗的证据基础 为患有物质使用障碍的个体患者做出决定。

Project Summary The goal of this application, submitted in response to PAR-19-368, “Accelerating the Pace of Drug Abuse Research Using Existing Data” is to leverage data from the NIDA Clinical Trials Network to enhance our understanding of treatment effect heterogeneity in psychosocial treatments for substance use disorders. Treatment effect heterogeneity is particularly a concern in research of substance use disorder treatments, in part due to heterogeneous sub-phenotypes of patients in symptom profile, disease course, and recovery trajectory. Nevertheless, analysis of treatment effect heterogeneity in substance use disorder research has been often conducted in a suboptimal manner using subgroup analysis (i.e., estimating impacts separately within groups defined by a single covariate), which could result in finding spurious differences in treatment effects by subgroup due to the performance of multiple statistical tests and random variability across patients. Another challenge of single covariate-based subgroup analysis is that most covariates have small moderating effects and their individual contribution to treatment effect heterogeneity is not meaningfully informative to treatment decisions. Our objective is to apply a novel statistical method, causal forest approach, to systematically examine treatment effect heterogeneity of psychosocial treatments for substance use disorders. This study uses data from 12 randomized controlled trials in the NIDA Clinical Trials Network which examined effectiveness of nine distinct psychosocial treatments against treatment-as-usual condition (Motivational Incentives, Motivational Enhancement Therapy, Screening Motivational Assessment, Therapeutic Education System, Brief Strategic Family Therapy, Twelve-Step Facilitation, Motivational Interviewing, Seeking Safety, and Exercise Program). For each type of psychosocial treatment, we propose to implement the causal forest approach to estimate the expected effect of a treatment for each individual while taking into account multiple covariates simultaneously. The estimated treatment effects will be used to test the presence and degree of treatment effect heterogeneity in each type of psychosocial treatment. Using the variable importance measure obtained from the causal forest, we also plan to identify the most important covariates contributing to treatment effect heterogeneity in each type of psychosocial treatment. These analyses will be repeated for multiple outcomes (e.g., abstinence, reduction in frequency of target substance use) to examine whether and how the degree of treatment effect heterogeneity as well as common effect moderators differ across outcomes. Overall, these analyses will grow the evidence base that can be used by treatment providers to guide treatment decisions for individual patients with substance use disorders.