Overcoming low representation: Lessons from an integrative data analysis of CTN studies for assessing treatment effectiveness among Black people who use cocaine and/or opioids [commentary].

Given the disproportionate impact of substance use on individuals, families, and communities from populations underrepresented in clinical trials, increasing their enrollment in treatment research is critical for ensuring that the findings inform policies and programs that are inclusive of all communities, thereby advancing health equity. However, since underrepresented groups continue to be underenrolled in clinical trials testing the efficacy and effectiveness of psychosocial treatments for substance use disorder, substance use researchers are still grappling with this challenge.

In this commentary, we describe rigorous methodological approaches, such as integrative data analysis (IDA) and related methods (e.g., moderated nonlinear factor analysis and propensity score weighting), that can help address the challenges posed by the underrepresentation of certain populations. By combining individual-level data from multiple studies into a pooled dataset, these methods increase sample size and statistical power while addressing covariate imbalance across treatment groups. We describe how we employed these methods to address the aims of our recently completed secondary data analysis project conducted within the National Drug Abuse Treatment Clinical Trial Network (CTN-0125; Integrative Data Analysis of CTN Studies to Examine the Impact of Psychosocial Treatments for Black People Who Use Cocaine and/or Opioids). Our study used these methods to pool and analyze data from nine completed CTN trials to assess the comparative effectiveness of psychosocial treatments for Black adults who use cocaine and/or opioids, a group underrepresented in registered trials of the NIDA. We illustrate the application of these methodological approaches in CTN-0125 and demonstrate how they complement each other to address unique analytic challenges. We describe how we addressed data harmonization challenges due to variations in data formats and inconsistencies or gaps in the supportive documentation available on the NIDA Data Share website. We conclude with recommendations for the research field on how to further address sample size and data integration challenges.

Related protocols: CTN-0125