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People in remission from substance use disorders (SUDs) have a history of using their own experience (also referred to as ''experiential knowledge'' or ''expertise'') to support those in or seeking SUD remission. In recent years, people with this experiential knowledge are being incorporated into research protocols to better guide research questions and inform the real-world uptake of SUD treatments and recovery supports. In these research contexts, however, those with research expertise and addiction rarely speak freely about these overlapping perspectives. The aim of this commentary is to increase awareness regarding the existence of this group (addiction researchers with addiction) and to explore the possibility that their expertise may help advance addiction science while helping to reduce stigma.

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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.