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Abstract: High levels of missing outcome data for biologically confirmed substance use (BCSU) threaten the validity of substance use disorder (SUD) clinical trials. Underlying attributes of clinical trials could explain BCSU missingness and identify targets for improved trial design. We reviewed 21 clinical trials funded by the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN) and published from 2005 to 2018 that examined pharmacologic and psychosocial interventions for SUD. We used configurational analysis-a Boolean algebra approach that identifies an attribute or combination of attributes predictive of an outcome-to identify trial design features and participant characteristics associated with high levels of BCSU missingness. Associations were identified by configuration complexity, consistency, coverage, and robustness. We limited results using a consistency threshold of 0.75 and summarized model fit using the product of consistency and coverage. For trial design features, the final solution consisted of two pathways: psychosocial treatment as a trial intervention OR larger trial arm size (complexity=2, consistency=0.79, coverage=0.93, robustness score=0.71). For participant characteristics, the final solution consisted of two pathways: interventions targeting individuals with poly- or nonspecific substance use OR younger age (complexity=2, consistency=0.75, coverage=0.86, robustness score=1.00). Conclusions: Psychosocial treatments, larger trial arm size, interventions targeting individuals with poly- or nonspecific substance use, and younger age among trial participants were predictive of missing BCSU data in SUD clinical trials. Interventions to mitigate missing data that focus on these attributes may reduce threats to validity and improve utility of SUD clinical trials. Related protocols: CTN-0002, CTN-0003, CTN-0004, CTN-0006, CTN-0007, CTN-0009. CTN-0013, CTN-0014, CTN-0015, CTN-0017, CTN-0021, CTN-0029, CTN-0030, CTN-0031, CTN-0037, CTN-0044, CTN-0046, CTN-0048, CTN-0051, CTN-0053

Abstract:

A lack of consensus on the optimal outcome measures to assess opioid use disorder (OUD) treatment efficacy and their precise definition and computation has hampered the pooling of research data for evidence synthesis and meta-analyses. This study aimed to empirically contrast multiple clinical trial definitions of treatment success by applying them to the same dataset.

Data analysis used a suite of functions, developed as a software package for the R language, to operationalize 61 treatment outcome definitions based on urine drug screening (UDS) results. Outcome definitions were derived from clinical trials that are among the most influential in the OUD treatment field. Outcome functions testing various medication for OUD (MOUD) options were applied to a dataset (n=2492) derived from the CTN-0094 project, which harmonized data from three large-scale National Drug Abuse Treatment Clinical Trials Network (CTN) studies (CTN-0027, CTN-0030, CTN-0051). Hierarchical clustering was employed to empirically contrast outcome definitions.

The optimal number of clusters identified was three. Cluster 1, comprising eight definitions focused on detecting opioid-positive UDS, did not include missing UDS in outcome calculations, potentially resulting in inflated rates of treatment success. Cluster 2, with the highest variability, included 10 definitions characterized by strict criteria for treatment success, relying heavily on UDS results from either a brief period or a single study visit. The 43 definitions in Cluster 3 represented a diverse range of outcomes, conceptualized as measuring abstinence, use reduction and relapse. These definitions potentially offer more balanced measures of treatment success or failure, as they avoid the extreme methodologies characteristic of Clusters 1 and 2.

Conclusions: Clinical trials using urine drug screening (UDS) for objective substance use assessment in outcome definitions should consider (1) incorporating missing UDS data in outcome computation and (2) avoiding over-reliance on UDS data confined to a short time frame or the occurrence of a single positive urine test following a period of abstinence.

Related protocols: CTN-0094

Abstract:

Objectives: Timeline follow-back (TLFB) is a self-report measure commonly used as a method of assessing historical drug use in both clinical and research settings. Our study considered rates of agreement between TLFB and an objective biological assay of opioid use.

Methods: We calculated the rates of agreement between negative report of opioid use for the most recent 8 days on TLFB and urine toxicology (UTOX) results in a large multisite opioid use disorder treatment trial (CTN-0051, X:BOT).

Results: In total, 3986 assessments were provided by trial participants with both UTOX and TLFB during weeks 1 to 12, 2716 during weeks 13 to 24, and 325 at week 28. Rates of disagreement between negative TLFB and positive opioid UTOX were 2.33% of all assessments (21.68% of those with positive UTOX) over weeks 1 to 12, 2.06% of all assessment (25.00% of those with positive UTOX) over weeks 13 to 24, and 9.85% of all assessments (26.02% of those with positive UTOX) at week 28.

Conclusions: Negative TLFB seems to be generally associated with negative results on urine toxicology.

Related protocols: CTN-0051

Abstract:

The agreement between self-reported cannabis abstinence with urine cannabinoid concentrations in a clinical trials setting is not well characterized. This study assessed the agreement between various cannabinoid cutoffs and self-reported abstinence across three clinical trials, one including contingency management for abstinence. All three of the trials included both participant self-report and weekly urine samples for cannabis and creatinine concentration measurements. Bootstrapped data were assessed for agreement between self-reported 7+ day abstinence and urine cannabinoid tests using generalized linear mixed effects models for clustered binary outcomes. One study implemented contingency management for cannabis abstinence. Four hundred and seventy-three participants with 3787 valid urine specimens were included. Urine was analyzed for 11-nor-9-carboxy-delta-9-tetrahydrocannabinol and creatinine using immunoassay methods Biological cutoffs of 50, 100, and 200 ng/ml, as well as changes in CN normalized THCCOOH (25%/50% decrease), were assessed for agreement with self-reported abstinence during the three clinical trials.

Results found that agreement between measured THCCOOH and self-reported abstinence increases with increasing cutoff concentrations, while the agreement with self-reported non-abstinence decreases with increasing cutoff concentrations. Combining THCCOOH cutoffs with recent changes in CN-THCCOOH provides a better agreement in those self-reporting abstinence. Participants in the studies that received CM for abstinence had a lower agreement between self-reported abstinence and return to use than those in studies that did not have a contingency management component.

Conclusions: Using a combination of both concurrent THCCOOH and recent changes in CN-THCCOOH, the agreement between self-reported cannabis abstinence initiation and measure agreement is shortened significantly.

Related protocols: CTN-0053

Abstract:

Quantifying cannabis use is complex due to a lack of a standardized packaging system that contains specified amounts of constituents. A laboratory procedure has been developed for estimating physical quantity of cannabis use by utilizing a surrogate substance to represent cannabis, and weighing the amount of the surrogate to determine typical use in grams. This secondary analysis used data from a multi-site, randomized, controlled pharmacological trial for adult cannabis use disorder (N=300), sponsored by the National Drug Abuse Treatment Clinical Trials Network (protocol CTN-0053), to test the incremental validity of this procedure. In conjunction with the Timeline Followback, this physical scale-based procedure was used to determine whether average grams per cannabis administration predicted urine cannabinoid levels (11-nor-9-carboxy-delta-9-tetrahydrocannabinol) and problems due to use, after accounting for self-reported number of days use (in the past 30 days) and number of administrations per day in a 12-week clinical trial for cannabis use disorder.

Likelihood ratio tests suggest that model fit was significantly improved when grams per administration and relevant interactions were included in the model predicting urine cannabinoid level and in the model predicting problems due to cannabis use, relative to a model that contained only simpler measures of quantity and frequency.

Conclusions: This study provides support for the use of a scale-based method for quantifying cannabis use in grams. This methodology may be useful when precise quantification is necessary, for example, for researchers to begin to establish meaningful cut-offs for high-risk cannabis use. Researchers may use grams per episode to determine clinical cut-offs for high-risk episodic use in terms of “standard joints,” similar to cut-offs developed in the alcohol literature. Precise quantification of cannabis use also offers some advantages over urine cannabinoid biomarker data, as it can be adapted for remote data collection and is better suited to detect variability in use patterns.

Related protocols: CTN-0053

Abstract:

Although research has generally supported the validity of substance use self-reports, some patients deny urine-verified substance use. This study examined the prevalence and patterns of denying urinalysis-confirmed opioid use in a sample of prescription opioid dependent patients. It also identified characteristics associated with denial in this population of increasing public health concern. Opioid use self-reports were compared with weekly urinalysis results in the National Drug Abuse Treatment Clinical Trials Network’s 12-week multi-site treatment study for prescription opioid dependence (CTN-0030 Prescription Opioid Addiction Treatment Study (POATS)). Among those who used opioids during the trial (n=246/360), 44.3% (n=109) denied urinalysis-confirmed opioid use, although usually only once (78%). Overall, 22.9% of opioid-positive urine tests (149/650) were denied on self-report. Multivariable analysis found that initially using opioids to relieve pain was associated with denying opioid use.

Conclusions: The present study shows that, although the clear majority of self-reports were consistent with urine results, many participants denied urine-confirmed use, albeit infrequently, despite knowing they would be tested. This result, combined with the finding that 7% of the positive self-reports were provided in a week with a negative urine test, shows the importance of obtaining both self-report data and urine tests; neither one alone is adequate.

Related protocols: CTN-0030

Abstract:

Using data from National Drug Abuse Treatment Clinical Trials Network protocol CTN-0003, “Suboxone Taper: A Comparison of Taper Schedules,” this study examined predictors of opiate abstinence status 3 months after the end of buprenorphine/naloxone treatment for opioid-dependent participants. Participants (n=516, age > 15 years), received buprenorphine/naloxone treatment for 4 weeks and then were randomly assigned to undergo dose tapering over either 7 or 28 days. Bivariate analysis was performed to identify possible predictors of successful opiate abstinence outcome (p-value < 0.10). Logistic regression analysis with backward stepwise selection was then performed to produce final model containing independent predictors at p-value < 0.05. Bivariate analysis identified several possible predictors, including: opioid and drug urine tests result at the end of taper; employment status, family problems, and alcohol use domains of the Addiction Severity Index (ASI) score; and the Clinical Opiate Withdrawal Scale (COWS) at the end of stabilization. The final predictor list identified by logistic regression included: ASI domains for family and alcohol problems, COWS at the end of stabilization, and opiate urine test at the end of taper.

Conclusions: In this analysis, participants presenting with a negative urine test for opiates at the end of the taper period, more severe alcohol or family problems (contrary to previous studies), or fewer symptoms of opiate withdrawal at the end of stabilization were more likely to have successful opiate abstinence.

Related protocols: CTN-0003

Abstract:

This study examined the impact of contingency management (CM) on stimulant use heterogeneity across two 12-week clinical trials, National Drug Abuse Treatment Clinical Trials Network protocols CTN-0006 and CTN-0007. The hypothesis was that CM effects on stimulant use would differ across multiple sub-groups of patients with distinct trajectories of use throughout the treatment period. The outcome of positive stimulant urine analysis (UA+) was measured two times per week for 12 weeks. Growth mixture modeling was used to estimate multiple latent class solutions (classes 1 through 6). The best fitting, clinically interpretable model was the 3-class linear model (BIC=7624). The model produced the following classes: Class 1 (21% of sample) = low probability (35%) of UA+ at baseline, steep decline in UA+ submissions during treatment. Class 2 (38%) = moderate probability of UA+ at baseline (42%), moderate decline in UA+ submissions over time caused by CM. Class 3 (41%) = high probability of UA+ at baseline (65%), increase in UA+ submissions over time and no effect of CM.

Conclusions: Identifying sub-groups may help explain heterogeneity in substance use trajectories and identify characteristics that could inform treatment nonresponse (e.g. Class 3). Such models could also assist with identifying segments of the stimulant use population who could benefit from ancillary services in order to more effectively impact abstinence.

Related protocols: CTN-0006, CTN-0007

Abstract:

A review of substance use clinical trials indicates that sub-optimal methods are the most commonly used procedures to deal with longitudinal missing information. In this study of data from the National Drug Abuse Treatment Clinical Trials Network buprenorphine protocol CTN-0003, listwise deletion (i.e., using complete cases only), positive urine analysis (UA) imputation, and multiple imputation (MI) were used to evaluate the effect of baseline substance use and buprenorphine/naloxone tapering schedule (7 or 28 days) on the probability of a positive UA (UA+) across the 4-week treatment period. The listwise deletion generalized estimating equations (GEE) model demonstrated that those in the 28-day taper group were less likely to submit a UA+ for opioids during the treatment period, as did the positive UA imputation model. The MI model also demonstrated a similar effect of taper group, but the effect size was more similar to that of the listwise deletion model.

Conclusions: The missing data situation described in this investigation generalizes to many other substance use psychopharmacology clinical trials wherein there is missing data on the outcome of interest only. Future researchers may find utilization of the MI procedure in conjunction with the common method of GEE analysis as a helpful analytic approach when the missing at random assumption is justifiable.

Related protocols: CTN-0003

Abstract:

Two common procedures for the treatment of missing information, listwise deletion and positive urine analysis (UA) imputation (e.g., if the participant fails to provide urine for analysis, then score the UA positive), may result in significant biases during the interpretation of treatment effects. To compare these approaches and to offer a possible alternative, these two procedures were compared to the multiple imputation (MI) procedure with publicly available data from a recent clinical trial (National Drug Abuse Treatment Clinical Trials Network protocol CTN-0003, Ling et al, 2009). Listwise deletion, single imputation (i.e., positive UA imputation), and MI missing data procedures were used to comparatively examine the effect of the protocol’s two different buprenorphine/naloxone tapering schedules (7- or 28-days) for opioid addiction on the likelihood of a positive UA. The listwise deletion of missing data resulted in a nonsignificant effect for the taper while the positive UA imputation procedure resulted in a significant effects, replicating the original findings by Ling et al (2009). Although the MI procedure also resulted in a significant effect, the effect size was meaningfully smaller and the standard errors meaningfully larger when compared to the positive UA procedure. This study demonstrates that the researcher can obtain markedly different results depending on how the missing data are handled. Missing data theory suggests that listwise deletion and single imputation procedures should not be used to account for missing information, and that MI has advantages with respect to internal and external validity when the assumption of missing at random can be reasonably supported. Consistent with previous investigation of missing data in substance abuse treatment, the authors encourage researchers to understand and report the missing data mechanism as well as use newer procedures for the treatment of missing information (i.e., MI or direct maximum likelihood procedures) that are based on a research-specified “best estimate” of the missing values.

Related protocols: CTN-0003

Abstract:

Selection of appropriate outcome measures is important for clinical studies of drug addiction treatment. Researchers use various methods for collecting drug use outcomes and must consider substances to be included in a urine drug screen (UDS), accuracy of self-report, use of various instruments and procedures for collecting self-reported drug use, and timing of outcome assessments. This study sought to define a set of candidate measures to (1) assess their intercorrelation and (2) identify any differences in results. To that end, data were combined from seven completed protocols in the National Drug Abuse Treatment Clinical Trials Network (CTN), with a total of 1897 participants. Nine outcome measures were defined, based on UDS, self-report, or a combination, then multivariable, multilevel generalized estimating equation models were used to assess subgroup differences in intervention success, controlling for baseline differences and accounting for clustering by CTN protocols. Results found high correlations among all candidate outcomes. All outcomes showed consistent overall results with no significant intervention impact on drug use during follow-up. However, with most UDS variables, but not with self-report or “corrected self-report,” a significant gender–ethnicity interaction with benefit shown in African American women, White women, and Hispanic men was observed.

Conclusions: Despite strong associations between candidate measures, important differences in results were found. This study demonstrates the potential utility and impact of combining UDS and self-report data for drug use assessment. The results suggest possible differences in intervention efficacy by gender and ethnicity, but highlight the need to cautiously interpret observed interactions. Additional studies like this one will help guide implementation of methodological recommendations to construct combined measures.

Abstract:

In clinical trials of treatment for stimulant abuse, including several National Drug Abuse Treatment Clinical Trials Network (CTN) protocols, researchers commonly record both Time-Line Follow-Back (TLFB) self-reports and urine drug screen (UDS) results. This study aimed to compare the power of self-report, qualitative (use vs. no use) UDS assessment, and various algorithms to generate self-report-UDS composite measures to detect treatment differences via t-test in simulated clinical trial data. Monte Carlo simulations, patterned in part on real data to model self-report reliability, were performed on UDS errors, dropout, informatively missing UDS reports, incomplete adherence to a urine donation schedule, temporal correlation of drug use, number of days in the study period, number of patients per arm, and distribution of drug-use probabilities. Investigated algorithms include maximum likelihood and Bayesian estimates, self-report alone, UDS alone, and several simple modifications of self-report (referred to here as ELCON algorithms) which eliminate perceived contradictions between it and UDS. Among the algorithms investigated, simple ELCON algorithms gave rise to the most powerful t-tests to detect mean group differences in stimulant drug use.

Conclusions: Further investigation is needed to determine if simple, naïve procedures such as the ELCON algorithms are optimal for comparing clinical study treatment arms. But researchers who currently require an automated algorithm in scenarios similar to those simulated for combining TLFB and UDS to test group differences in stimulant use should consider one of the ELCON algorithms. This analysis continues a line of inquiry which could determine how best to measure outpatient stimulant use in clinical trials.

Abstract:

No consensus is available for identifying the best primary outcome for substance use disorder trials, making interpretation across trials difficult. Abstinence is the most desirable treatment outcome although a wide variety of other endpoints have been used. This report provides a framework for determining an optimal primary endpoint and the relevant measurement approach for substance use disorder treatment trials. The framework was developed based on a trial for stimulant abuse using exercise as an augmentation treatment, delivered within the NIDA Clinical Trials Network (protocol CTN-0037). The use of a common endpoint across trials will facilitate comparisons of treatment efficacy. Primary endpoint options in existing substance abuse studies were evaluated. This evaluation included surveys of the literature for endpoints and measurement approaches, followed by assessment of endpoint choices against study design issues, population characteristics, tests of sensitivity, and tests of clinical meaningfulness.

Conclusion: We concluded that the best current choice for a primary endpoint is percent days abstinent, as measured by the Time Line Follow Back interview conducted three times a week with recall aided by a take-home Substance Use Diary. To improve the accuracy of the self-reported drug use, the results of qualitative urine drug screens will be used in conjunction with the Time Line Follow Back results. There is a need for a standardized endpoint in this field to allow for comparison across treatment studies, and we suggest that the recommended candidate endpoint be considered. However, the study design and goals ultimately must guide the final decision.

Related protocols: CTN-0037