# Treatment Effect Heterogeneity in Psychosocial Treatments for Substance Use Disorders

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $286,563

## Abstract

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.

## Key facts

- **NIH application ID:** 10363799
- **Project number:** 1R01DA053202-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Ryoko Susukida
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $286,563
- **Award type:** 1
- **Project period:** 2022-09-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10363799

## Citation

> US National Institutes of Health, RePORTER application 10363799, Treatment Effect Heterogeneity in Psychosocial Treatments for Substance Use Disorders (1R01DA053202-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10363799. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*