权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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.

项目总结