Application of Causal Forest Model to Examine Treatment Effect Heterogeneity in Substance Use Disorder Psychosocial Treatments

• Published in: International journal of methods in psychiatric research Vol. 34; no. 1; pp. e70011 - n/a
• Main Authors: Susukida, Ryoko, Amin‐Esmaeili, Masoumeh, Badillo‐Goicoechea, Elena, Nguyen, Trang Q., Stuart, Elizabeth A., Rosenblum, Michael, Dunn, Kelly E., Mojtabai, Ramin
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2025; John Wiley and Sons Inc; Wiley
• Subjects: Abstinence; Adult; causal forest; Clinical trials; Drug abuse; Drug addiction; Drug use; Female; Forests; heterogeneity; Humans; Male; Models, Statistical; Original; Outcome Assessment, Health Care; Patients; Psychosocial Intervention - methods; psychosocial interventions; Randomized Controlled Trials as Topic; Substance abuse treatment; Substance use; Substance use disorder; substance use disorder treatment; Substance-Related Disorders - therapy; Toxicology; treatment effect; Treatment Effect Heterogeneity; Urine; substance use disorder treatment; treatment effect; causal forest; psychosocial interventions; heterogeneity
• ISSN: 1049-8931; 1557-0657; 1557-0657
• DOI: 10.1002/mpr.70011

ABSTRACT Objectives Heterogeneity of treatment effect (HTE) is a concern in substance use disorder (SUD) treatments but has not been rigorously examined. This exploratory study applied a causal forest approach to examine HTE in psychosocial SUD treatments, considering multiple covariates simultaneously. Methods Data from 12 randomized controlled trials of nine psychosocial treatments were obtained from the National Institute on Drug Abuse Clinical Trials Network. Using causal forests, we estimated the conditional average treatment effect (CATE) on drug abstinence. To assess HTE, we compared CATE variance against total outcome variability, conducted an omnibus test, and applied the Rank‐Weighted Average Treatment Effect (RATE). Results Across nine interventions, CATE variance was lower than total outcome variability, indicating lack of strong evidence of HTE with respect to the baseline covariates considered. The omnibus test and RATE analysis generally support this finding. However, the RATE analysis identified potential HTE in a motivational interviewing trial; this could be a false positive given the multiple analyses; replication is needed to confirm this. Conclusions While causal forests show utility in exploring HTE in SUD interventions, limited baseline assessments in most trials suggest a cautious interpretation. The RATE findings for motivational interviewing highlight potential subgroup‐specific treatment benefits, warranting further research.