Abstract

As contingency management (CM) moves from research to practice, researchers have a responsibility to outline the minimum procedural necessities that lead to an effective, sustainable treatment that can be implemented as a mainstream therapy for substance use disorders. To begin identifying the minimum requirements, the purpose of the current study was to provide framework and a first step toward building a risk calculator that predicts treatment outcomes in CM, and can predict the optimal incentive size to prescribe by evaluating behavioral economic factors, demographic variables, and use severity measures in individuals who completed CM treatment for alcohol use. Participants were 38 individuals enrolled in the active treatment arms of two parent CM studies for reducing alcohol use (Koffarnus et al., 2018; Koffarnus et al., 2021). Participants were 42 years old on average, 55% male, and a majority were white, non-Hispanic. Fifteen candidate predictor variables were assessed for inclusion in the predictive model including demographic variables, use severity scores, and behavioral economic parameters. A logistic regression framework was used to identify top predictive models. Accuracy was assessed by computing receiver operating characteristic (ROC) curves and area under the curves. A model including the delay discounting parameter, log 10 (ED50) of alcohol, participant age, and Beck’s Anxiety Inventory score was predictive of treatment outcomes in the current sample. The results demonstrate the utility of the ROC analysis as a method for identifying a predictive model. Further research is needed to replicate and verify the findings of the current analysis.

Document Type

Article

Publication Date

2026

Notes/Citation Information

© The Author(s) 2026

Digital Object Identifier (DOI)

https://doi.org/10.1007/s40732-025-00671-y

Funding Information

Haily Traxler’s time was supported by a fellowship to Haily Traxler under the Clinical and Translational Science of the National Institutes of Health award number TL1TR001997 and a grant from the National Institute on Drug Abuse (T32DA035200) of the National Institutes of Health. The research reported in this article was supported by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under award numbers R21AA022727 (Koffarnus et al., 2018) and R21AA023605 (Koffarnus et al., 2021) to Mikhail N. Koffarnus. 100% of this research was supported by federal money with no financial or nonfinancial support from nongovernmental sources. The funding agencies had no role in the study design, collection, analysis of the data, manuscript preparation, or submission of this manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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