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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要该申请的目的是响应于第19-368款提交的“加速滥用药物的步伐使用现有数据的研究”是利用NIDA临床试验网络的数据来增强我们理解治疗效果的心理治疗中的异质性。治疗效应异质性在研究物质使用障碍治疗的研究中尤其关注部分是由于症状特征，疾病病程和康复中患者的异质亚表征所致弹道。然而，对治疗效应的分析在药物使用障碍研究中的异质性具有通常是使用亚组分析以次优的方式进行的（即分别估计影响在一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次统计检验的性能和患者的随机变异性，子组的影响。基于协变量的亚组分析的另一个挑战是，大多数协变量的调节较小影响及其对治疗效应异质性的个人贡献对治疗决定。我们的目标是将新颖的统计方法（因果林方法）应用于系统地检查治疗效果的心理治疗物质使用障碍的异质性。本研究使用NIDA临床试验网络中的12项随机对照试验的数据，该试验检查了检查九种截然不同的社会心理治疗的有效性，以应对治疗态度（激励性）激励措施，动机增强疗法，筛查动机评估，治疗教育系统，简短的战略家庭疗法，十二步的便利，动机访谈，寻求安全，和锻炼计划）。对于每种类型的社会心理治疗，我们建议实施因果森林考虑多个人，以估算每个人的治疗的预期效果协变量很简单。估计的治疗效果将用于测试存在和程度治疗效应在每种类型的社会心理治疗中的异质性。使用可变的重要性度量从因果林获得的，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社会心理治疗中效应异质性。这些分析将重复多个结果（例如禁欲，靶向物质使用频率的降低），以检查是否以及如何以及如何治疗程度的效果异质性以及共同效应主持人在跨结果之间不同。全面的，这些分析将提高治疗提供者可以使用的证据基础，以指导治疗对患有药物使用障碍的个别患者的决定。