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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

Background and aims: Despite similar substance use levels, Black adults experience greater family, legal, employment and other social-contextual challenges related to recovery than other groups. Substance use treatments that address both substance use and social-contextual factors are uniquely positioned to address these substance-related problems and produce more sustainable improvements in social functioning than treatment as usual (TAU) or behavioral controls (Control). The aim of this study was to evaluate changes in substance-related problems among Black adults, focusing on the comparative effectiveness between social-contextual treatments and TAU/Control.

Design: Individual-level data synthesis based on secondary analysis of Black adults enrolled in the National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN).

Setting: All data were collected in the primary studies between 2001 and 2008 at clinics across the United States.

Participants: Black adults who reported cocaine and/or opioid use across nine studies within the NIDA CTN. The sample used herein consisted of individuals from five of these studies who provided data on substance-related problems (n=532; mean age=39.34; standard deviation=9.6).

Measurements: There were two treatment conditions: Social-contextual (e.g. Motivational Interviewing, Seeking Safety, STAGE 12) and TAU/Control. Moderated nonlinear factor analysis estimated latent scores for substance-related problems, using subscales from the Addiction Severity Index, while accounting for measurement noninvariance across studies, time and covariates. Linear mixed models estimated latent score differences over time between social-contextual treatments and TAU/Control during treatment and from the end of treatment through 12-month follow-up.

Findings: Both treatment groups improved across substance-related problem areas from baseline to the end-of-treatment (Cohen’s d = -0.10 to d = -0.47), with effects maintained at 12-month follow-up. Although social-contextual treatments did not statistically significantly outperform TAU/Control from baseline to end-of-treatment, they showed greater effects from end of treatment to 12-month follow-up in family/social [Cohen’s d difference ( d) = -0.47, 95% confidence interval (CI) = -0.57 to -0.38], legal ( d = -0.20, 95% CI = -0.31 to -0.10) and psychiatric problems ( d = 0.29, 95% CI = -0.38 to -0.20) than TAU/Control. Sensitivity analyses indicated that Seeking Safety and STAGE 12 predominantly drove post-treatment improvements in family/social problems.

Conclusions: Substance use treatment may yield broader, delayed benefits beyond substance use reduction among Black adults in the United States. Compared with treatment-as-usual, social-contextual treatments can yield more sustainable effects in legal, family and psychiatric areas among Black adults, with interventions such as Seeking Safety and STAGE 12 showing particular benefits in addressing family-related challenges.

Related protocols: CTN-0125

This is the Primary Outcomes Article for CTN-0125.

Cocaine- and opioid-related overdose deaths have increased among Black people, which makes identifying effective treatments for Black people a high priority. We investigated the comparative effectiveness of behavioral treatments among Black adults who use cocaine and/or opioids.

Methods: Identified multisite randomized clinical trials (RCTs) of behavioral interventions that targeted substance use, had participants who self-identified as Black, and included cocaine use outcome measures from the National Drug Abuse Treatment Clinical Trials Network (CTN) datashare. We estimated cocaine use and opioid use severity scale scores while considering study-level measurement non-invariance. Then, we estimated the inverse probability of treatment-weighted (IPTW) linear mixed models to assess comparative effectiveness of treatments that address social-contextual factors and those focused solely on substance use (e.g., contingency management (CM)) relative to treatment-as-usual/controls on cocaine use and opioid use severity scores during- and post-treatment.

Results: Nine RCTs met inclusion criteria, with a combined sample of 1,381 Black adults who used cocaine and/or opioids. The IPTW linear mixed models indicated that cocaine use severity decreased from baseline to end-of-treatment across three treatment groups, with a greater decrease for social-contextual treatments and CM relative to treatment-as-usual/controls. However, this greater reduction was maintained at 12-month follow-ups for social-contextual treatments, while CM worsened relative to TAU/controls. We found decreases in latent opioid use severity with no or minor differences between treatment groups.

Conclusions: The findings suggest that addressing social-contextual factors is an essential treatment component for long-term reduction of cocaine use among Black adults.

Related protocols: CTN-0125

Several large-scale, pragmatic clinical trials on opioid use disorder (OUD) have been completed in the National Drug Abuse Treatment Clinical Trials Network (CTN). However, the resulting data have not been harmonized between the studies to compare the patient characteristics. This paper provides lessons learned from a large-scale harmonization process that are critical for all biomedical researchers collecting new data and those tasked with combining datasets.

Researchers harmonized data from multiple domains from CTN-0027 (N = 1269), which compared methadone and buprenorphine at federally licensed methadone treatment programs; CTN-0030 (N = 653), which recruited patients who used predominantly prescription opioids and were treated with buprenorphine; and CTN-0051 (N = 570), which compared buprenorphine and extended-release naltrexone (XR-NTX) and recruited from inpatient treatment facilities. Patient-level data were harmonized and a total of 23 database tables, with meticulous documentation, covering more than 110 variables, along with three tables with “meta-data” about the study design and treatment arms, were created. Domains included: social and demographic characteristics, medical and psychiatric history, self-reported drug use details and urine drug screening results, withdrawal, and treatment drug details.

In this paper, the authors summarize the numerous issues with the organization and fidelity of the publicly available data which were noted and resolved, and present results on patient characteristics across the three trials and the harmonized domains, respectively. A systematic harmonization of OUD clinical trial data can be accomplished, despite heterogeneous data coding and classification procedures, by standardizing commonly assessed characteristics. Similar methods, embracing database normalization and/or “tidy” data, should be used for future datasets in other substance use disorder clinical trials.

Related protocols: CTN-0027, CTN-0030, CTN-0051

This study aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature. Data were obtained from six trials representing 1,777 individuals with OUD in the NIDA Clinical Trials Network (CTN-0001, -0002, -0009. -0030, -0049, and -0051). Researchers implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3 L). They performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms.

There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively.
Conclusions: Researchers for this study observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.

Related protocols: CTN-0001, CTN-0002, CTN-0009, CTN-0030, CTN-0049, CTN-0051

Suicide is the tenth leading cause of death in the United States and continues to be a major public health concern. Suicide risk is highly prevalent among individuals with co-occurring substance use disorders (SUD) and mental health disorders, making them more prone to adverse substance use related outcomes including overdose. Identifying individuals with SUD who are suicidal, and therefore potentially most at risk of overdose, is an important step to address the synergistic epidemics of suicides and overdose fatalities in the United States. The current study assesses whether patterns of suicidality endorsement can indicate risk for substance use and overdose.

Latent class analysis (LCA) was used to assess patterns of item level responses to the Concise Health Risk Tracking Self-Report (CHRT-SR), which measures thoughts and feelings associated with suicidal propensity. We used data from 2,541 participants with SUD who were enrolled across 8 randomized clinical trials in the National Drug Abuse Treatment Clinical Trials Network from 2012 to 2021 (CTN-0037, -0049, -0051, -0053, -0054, -0064, -0067, -0068). Characteristics of individuals in each class were assessed, and multivariable logistic regression was performed to examine class membership as a predictor of overdose. LCA was also used to analyze predictors of substance use days.

Three classes were identified and discussed: Class (1) Minimal Suicidality, with low probabilities of endorsing each CHRT-SR construct; Class (2) Moderate Suicidality, with high probabilities of endorsing pessimism, helplessness, and lack of social support, but minimal endorsement of despair or suicidal thoughts; and Class (3) High Suicidality with high probabilities of endorsing all constructs. Individuals in the High Suicidality class comprise the highest proportions of males, Black/African American individuals, and those with a psychiatric history and baseline depression, as compared with the other two classes. Regression analysis revealed that those in the High Suicidality class are more likely to overdose as compared to those in the Minimal Suicidality class (p = 0.04).

Conclusions: Suicidality is an essential factor to consider when building strategies to screen, identify, and address individuals at risk for overdose. The integration of detailed suicide assessment and suicide risk reduction is a potential solution to help prevent suicide and overdose among people with SUD.

Related protocols: CTN-0037, CTN-0049, CTN-0051, CTN-0053, CTN-0054, CTN-0064, CTN-0067, CTN-0068

The HIV/AIDS epidemic remains a major public health concern since the 1980s; untreated HIV infection has numerous consequences on quality of life. To optimize patients’ health outcomes and to reduce HIV transmission, this study, using data from CTN-0049 and CTN-0064, focused on vulnerable populations of people living with HIV (PLWH) and compared different predictive strategies for viral suppression using longitudinal or repeated measures.

The four methods of predicting viral suppression are (1) including the repeated measures of each feature as predictors, (2) utilizing only the initial (baseline) value of the feature as predictor, (3) using the last observed value as the predictors and (4) using a growth curve estimated from the features to create individual-specific prediction of growth curves as features. These models were compared using Synthetic Random Forests (SRF).

The SRF models predicted HIV viral suppression in CTN-0064 with an accuracy rate as high as 70%. The person-specific trajectories (Model 4) had the best predictive performance of the approaches. Not surprisingly, among the other models, those with characteristics from closer time-points produced better model fit than using baseline aspects only.

Conclusions: The model with person-specific trajectories had the best predictive power as compared to other models. The findings from this study are valuable, since they provide evidence that incorporating not just levels of predictors but also their change over time improves predictive performance of our models. Using person-specific intercepts and slopes provides a novel and useful approach to creating predictive models using repeated measurements. It also suggests the possibility of incorporating these types of modeling efforts into ongoing clinical monitoring using medical records.

Related protocols: CTN-0049, CTN-0064

The lack of a consensus on empirically supported and clinically meaningful outcome measures for stimulant use disorders (SUDs) continues to undermine the development and evaluation of effective behavioral and pharmacological treatment options. The aim of this study was to evaluate the clinical relevance of four stimulant use treatment outcome measures (longest curation of abstinence, percent of negative urinalysis submitted, abstinent in the last 2 weeks of treatment, and 3 or more weeks of continuous abstinence) by exploring their utility via association with stimulant and alcohol use, employment and legal problems, and severity of psychiatric symptomatology collected at follow-up.

Data used in these secondary analyses came from a multisite randomized contingency management treatment trial for SUDs (n=441) conducted through the NIDA Clinical Trials Network (CTN-0006). Multiple regression analyses were conducted to explore the association of 4 stimulant use treatment outcome measures and 8 3-month follow-up outcomes. Both dichotomous outcome measures showed similar performances being significantly associated with 4 follow-up outcomes. All outcome measures were consistently associated with better outcome responses at the 3-month follow-up, adding support to their clinical relevance and their adoption in SUD treatment trials. The two dichotomous outcome measures are reliable candidates to be used as endpoint outcomes, as recommended by the U.S. Food and Drug Administration (FDA).

Conclusions: The identification of clinically meaningful indicators of treatment response can promote important advances in the development of more effective treatments for stimulant use disorders (SUDs). These findings offer empirical support for the use of specific treatment outcome measures by determining their associations to clinically relevant 3-month follow-up outcomes.

Related protocols: CTN-0006

There are a wide variety of methods for using combustible cannabis which may impact an individual’s pattern of use as well as their response to cannabis use disorder (CUD) treatment. Previous research has noted racial/ethnic differences in cannabis users’ preferred method of use.

This study examined data from a randomized placebo-controlled trial of a pharmacological intervention for adults with CUD (CTN-0053). Latent profile analysis classified participants (N=302) based on their primary method of combustible cannabis use.

A four profile solution emerged that identified participants who demonstrated 1) Primarily Joint (n=50), 2) Primary Blunt (n=106), 3) Mixed Method of Use (MoU; n=30), and 4) Primarily Pipe (i.e. pipe or bong; n=116) use. Profiles were compared on socio-demographic characteristics and racial differences were found among the four latent profiles as well as differences in their level of use. Cannabis users with a preference for joints were more likely to be White as compared to other racial groups. In contrast, a greater proportion of participants with a preference for blunts were African American. The Primarily Joint profile was found to have the highest cannabis relapse rate at 1-month follow-up (94%) which was significantly greater than the Mixed MoU (74%) and Primarily Pipe (78%) profiles. Interestingly, there was no difference in 1-month follow-up cannabis relapse rates between the Primarily Joint and Primarily Blunt profiles (87%).

Conclusions: Findings suggest that treatment-seeking individuals who primarily use joints or blunts may face unique challenges that may impact cannabis abstinence. Along with other cannabis-related characteristics, an individual’s preferred method of use may represent an important factor to consider in the treatment of CUD.

Related protocols: CTN-0053

This study aimed to assess the validity of the psychiatric problems subscale of the Addiction Severity Index (ASI-psych) to ascertain psychiatric comorbidity among individuals participating in randomized controlled trials (RCTs) of substance use disorder (SUD) treatments.

The ASI-psych score among 1,660 RCT participants of National Institute of Drug Abuse Clinical Trials Network studies was compared against diagnosis of any serious mental disorder based on the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (SCID) or Mini-International Neuropsychiatric Interview (MINI). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) for detecting any serious mental disorders were estimated by the receiver operating characteristic (ROC) analysis.

Based on the overall sample, the AUC score for any serious mental disorder was 0.72 (95% confidence interval [CI], [0.69, 0.75]) with the optimal ASI-psych score of 24.6. There was no statistically significant difference in AUCs based on the SCID and MINI or by target drugs of RCTs.

Conclusions: Results support the utility of the ASI in screening for psychiatric comorbidity among patients receiving SUD treatments in RCT settings.

Related protocols: CTN-0005, CTN-0037

There is growing concern regarding the generalizability of findings from randomized controlled trials (RCTs) of interventions for substance use disorders (SUDs). This study used a selection model approach to assess and improve the generalizability of an evaluation for a web-based SUD intervention by making the trial sample resemble the target population.

The sample of the web-based SUD intervention (Therapeutic Education System vs. treatment-as-usual; n=507) was compared with the target population of SUD treatment-seeking individuals from the Treatment Episodes Data Set-Admissions (TEDS-A). Using weights based on the probabilities of RCT participation, weighted treatment effects on retention and abstinence were computed.

Substantial differences between the RCT sample and the target population were demonstrated in significant difference in the mean propensity scores (1.62 standard deviations at p<.001). The population effect on abstinence (12 weeks and 6 months) was statistically insignificant after weighting the data with the generalizability weight.

Conclusions: Findings from this study provide insights into the differences between RCT participants of a web-based SUD intervention and the target population from recent years. The results of this study also indicate how poor sample representativeness of the RCT compared with the target population impacted the observed findings of the web-based SUD intervention. Given the great potential for scalability of web-based SUD interventions, the representativeness of the sample with regard to the target population of potential users for the intervention should be carefully considered. Additionally, with a careful consideration of the treatment effect modifiers which are over- or under-representing in RCTs, application of generalizability weights could be a potentially useful tool for assessing and improving the generalizability of the findings from RCTs when the RCT sample does not well-represent the target population.

Related protocols: CTN-0044

The NIDA Data Share website was created to make CTN data available to the scientific community and the public at large. These data can be used for secondary analyses and thus serve to increase the contribution of the original studies conducted and extend findings into new areas and knowledge for the benefit of public health. This one-hour webinar, presented by Abigail Matthews, PhD, of the Emmes Corporation, and Dikla Shmueli-Blumberg, PhD, of the CTN Clinical Coordinating Center, focused on an overview of the website, provided highlights of its structure and contents, and detailed the technical procedures involved in utilizing the data for secondary analyses.

Additional Resources:

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This session presented information about the CTN Public Data Share website (now called the NIDA Data Share website), an electronic environment that allows data from completed clinical trials to be distributed to investigators and the public in order to promote new research, encourage further analyses, and disseminate information to the community. Secondary analyses produced from data sharing multiply the scientific contribution of the original research, optimizing the use of resources invested in the trials, as well as increasing scientific productivity.

Presentations in the session described the methods and results of secondary analyses conducted using the datasets, as well as the value of conducting secondary analyses to advance knowledge of evidence-based treatments.

Speakers included:

Overview of CTN Datashare – Carmen Rosa, MS, CCTN, NIDA

Practical Approaches for Using the CTN Datashare: Proposing Parameters, Creating Data Tables, and Analyzing Results – Abigail G. Matthews, PhD, the EMMES Corporation

Using the CTN Datashare: An Example Utilizing Data from Multiple Protocols – Audrey J. Brooks, PhD, University of Arizona

Secondary Analyses: Value and Limitations – Daniel J. Feaster, PhD, University of Miami Miller School of Medicine

This session presented information about the CTN Public Data Share website (now called the NIDA Data Share website), an electronic environment that allows data from completed clinical trials to be distributed to investigators and the public in order to promote new research, encourage further analyses, and disseminate information to the community. Secondary analyses produced from data sharing multiply the scientific contribution of the original research, optimizing the use of resources invested in the trials, as well as increasing scientific productivity.

Presentations in the session described the methods and results of secondary analyses conducted using the datasets, as well as the value of conducting secondary analyses to advance knowledge of evidence-based treatments.

Speakers included:

Overview of CTN Datashare – Carmen Rosa, MS, CCTN, NIDA

Practical Approaches for Using the CTN Datashare: Proposing Parameters, Creating Data Tables, and Analyzing Results – Abigail G. Matthews, PhD, the EMMES Corporation

Using the CTN Datashare: An Example Utilizing Data from Multiple Protocols – Audrey J. Brooks, PhD, University of Arizona

Secondary Analyses: Value and Limitations – Daniel J. Feaster, PhD, University of Miami Miller School of Medicine

There are many benefits of data sharing, including the promotion of new research from effective use of existing data, replication of findings through re-analysis of pooled data files, meta-analysis using individual patient data, and reinforcement of open scientific inquiry. A randomized controlled trial is considered the “gold standard” for establishing treatment effectiveness, but clinical trial research is very costly — sharing data is an opportunity to expand the investment of the clinical trial beyond its original goals at minimal cost. This article describes the goals, developments, and usage of the Data Share website for the National Drug Abuse Treatment Clinical Trials Network (CTN) in the U.S., including lessons learned, limitations, and major revisions, and considerations for future directions to improve data sharing. Since its inception in 2006 and through October 2012, nearly 1700 downloads from 27 clinical trials have been accessed from the Data Share website, with use increasing over the years. Individuals from 31 countries have downloaded data from the site, and there have been at least 13 publications derived from analysis of data obtained through the public Data Share website. Limitations of the website include minimal control over data requests and usage, which has resulted in little information and lack of control regarding how the data from the website are being used, and a lack of uniformity in data elements collected across CTN trials, which has limited cross-study analyses.

Conclusions: The Data Share website offers researchers easy access to de-identified data files with the goal to promote additional research and identify new findings from completed CTN studies. To maximize the utility of the website, ongoing collaborative efforts are needed to standardize the core measures used for data collection in the CTN studies with the goal of increasing their comparability and facilitating the ability to pool data files for cross-study analyses.

Supported by the Duke Clinical Research Institute (CTN DSC 1).