Beyond abstinence and relapse: cluster analysis of drug-use patterns during treatment as an outcome measure for clinical trials.
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| Title: | Beyond abstinence and relapse: cluster analysis of drug-use patterns during treatment as an outcome measure for clinical trials. |
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| Authors: | Panlilio, Leigh V. (AUTHOR), Stull, Samuel W. (AUTHOR), Bertz, Jeremiah W. (AUTHOR), Burgess-Hull, Albert J. (AUTHOR), Kowalczyk, William J. (AUTHOR), Phillips, Karran A. (AUTHOR), Epstein, David H. (AUTHOR), Preston, Kenzie L. (AUTHOR) |
| Source: | Psychopharmacology. Nov2020, Vol. 237 Issue 11, p3369-3381. 13p. 1 Color Photograph, 2 Graphs, 2 Maps. |
| Subjects: | Cluster analysis (Statistics), Drug abstinence, Treatment effectiveness, Clinical trials, Substance-induced disorders, Opioid abuse |
| Abstract: | Rationale: Many people being treated for opioid use disorder continue to use drugs during treatment. This use occurs in patterns that rarely conform to well-defined cycles of abstinence and relapse. Systematic identification and evaluation of these patterns could enhance analysis of clinical trials and provide insight into drug use. Objectives: To evaluate such an approach, we analyzed patterns of opioid and cocaine use from three randomized clinical trials of contingency management in methadone-treated participants. Methods: Sequences of drug test results were analyzed with unsupervised machine-learning techniques, including hierarchical clustering of categorical results (i.e., whether any samples were positive during each week) and K-means longitudinal clustering of quantitative results (i.e., the proportion positive each week). The sensitivity of cluster membership as an experimental outcome was assessed based on the effects of contingency management. External validation of clusters was based on drug craving and other symptoms of substance use disorder. Results: In each clinical trial, we identified four clusters of use patterns, which can be described as opioid use, cocaine use, dual use (opioid and cocaine), and partial/complete abstinence. Different clustering techniques produced substantially similar classifications of individual participants, with strong above-chance agreement. Contingency management increased membership in clusters with lower levels of drug use and fewer symptoms of substance use disorder. Conclusions: Cluster analysis provides person-level output that is more interpretable and actionable than traditional outcome measures, providing a concrete answer to the question of what clinicians can tell patients about the success rates of new treatments. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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