Treatment Effect Heterogeneity in Psychosocial Treatments for Substance Use Disorders

药物使用障碍心理社会治疗的治疗效果异质性

• 批准号: 10683066
• 负责人: Ryoko Susukida
• 金额: $ 28.66万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683066
• 关键词: Abstinence; Acceleration; Address; Age; Algorithms; Clinical; Clinical Trials Network; Data; Data Analyses; Disease; Drug abuse; Education; Family psychotherapy; Frequencies; Goals; Health Personnel; Heterogeneity; Incentives; Individual; Intervention; Measures; Methodology; Methods; Motivation; National Institute of Drug Abuse; Outcome; Participant; Patients; Performance; Persons; Phenotype; Provider; Randomized, Controlled Trials; Recovery; Research; Risk; Safety; Severities; Statistical Methods; Subgroup; Substance Use Disorder; Symptoms; System; Techniques; Testing; Therapeutic; Treatment outcome; Work; clinically relevant; data harmonization; data sharing; drug abstinence; effectiveness evaluation; evidence base; exercise program; experience; forest; individual patient; individual variation; motivational enhancement therapy; novel; polysubstance use; precision medicine; prevent; psychiatric comorbidity; psychosocial; response; screening; sex; shared repository; sociodemographics; substance use; substance use treatment; treatment as usual; treatment effect; treatment research

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.

项目摘要 根据PAR-19-368提交的这份申请书的目标是“加快药物滥用的步伐” 利用现有数据进行研究“是利用来自NIDA临床试验网络的数据来增强我们的 物质使用障碍心理社会治疗中治疗效果异质性的理解。 在物质使用障碍治疗的研究中，治疗效果的异质性尤其令人关注 部分是由于患者在症状特征、病程和康复方面的不同亚型 弹道。然而，物质使用障碍研究中治疗效果的异质性分析已经 通常使用子组分析以次优的方式进行(即，单独估计影响 在由单个协变量定义的组内)，这可能导致在治疗中发现虚假差异 由于多项统计检验的表现和患者之间的随机变异性，按亚组划分的效果。 基于单协变量的亚组分析的另一个挑战是，大多数协变量具有小的调节作用 效应及其个体对治疗效应的贡献异质性对 治疗决定。我们的目标是将一种新的统计方法--因果森林方法应用于 系统考察物质使用障碍心理社会治疗效果的异质性。 这项研究使用了NIDA临床试验网络中12个随机对照试验的数据，这些试验检查了 九种不同的心理社会治疗对照常治疗(激励性)的有效性 激励，动机增强疗法，筛选动机评估，治疗性教育 系统，简明战略性家庭治疗，十二步促进，激励性访谈，寻求安全， 和锻炼计划)。对于每种类型的心理社会治疗，我们建议实施因果森林 一种估计每个个体的治疗预期效果的方法，同时考虑多个因素 协变量同时存在。估计的治疗效果将被用来测试是否存在和程度 治疗效果在每种类型的心理社会治疗中的异质性。使用可变重要性度量 从因果森林中获得，我们还计划确定对治疗有贡献的最重要的协变量 影响每种类型的心理社会治疗的异质性。这些分析将针对多个 结果(例如，戒断、目标物质使用频率的减少)，以检查是否以及如何 治疗效果的程度、异质性以及共同效果调节因子因结果不同而不同。总的来说， 这些分析将扩大治疗提供者可以用来指导治疗的证据基础 为个别有药物使用障碍的患者做出决定。