Matthew R Pearson1, Adrian J Bravo2, Megan Kirouac3, Katie Witkiewitz4. 1. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States. Electronic address: mateo.pearson@gmail.com. 2. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States. Electronic address: ajbravo@unm.edu. 3. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States; Department of Psychology, University of New Mexico, United States. Electronic address: mkirouac@unm.edu. 4. Center on Alcoholism, Substance Abuse, and Addictions, University of New Mexico, 2650 Yale Blvd SE MSC 11-6280, Albuquerque, NM 87106, United States; Department of Psychology, University of New Mexico, United States. Electronic address: katiew@unm.edu.
Abstract
BACKGROUND: To examine whether a clinically meaningful alcohol consumption cutoff can be created for clinical samples, we used receiver operator characteristic (ROC) curves to derive gender-specific consumption cutoffs that maximized sensitivity and specificity in the prediction of a wide range of negative consequences from drinking. METHODS: We conducted secondary data analyses using data from two large clinical trials targeting alcohol use disorders: Project MATCH (n=1726) and COMBINE (n=1383). RESULTS: In both studies, we found that the ideal cutoff for men and women that maximized sensitivity/specificity varied substantially both across different alcohol consumption variables and alcohol consequence outcomes. Further, the levels of sensitivity/specificity were poor across all consequences. CONCLUSIONS: These results fail to provide support for a clinically meaningful alcohol consumption cutoff and suggest that binary classification of levels of alcohol consumption is a poor proxy for maximizing sensitivity/specificity in the prediction of negative consequences from drinking. Future research examining consumption-consequence associations should take advantage of continuous measures of alcohol consumption and alternative approaches for assessing the link between levels of consumption and consequences (e.g., ecological momentary assessment). Clinical researchers should consider focusing more directly on the consequences they aim to reduce instead of relying on consumption as a proxy for more clinically meaningful outcomes.
RCT Entities:
BACKGROUND: To examine whether a clinically meaningful alcohol consumption cutoff can be created for clinical samples, we used receiver operator characteristic (ROC) curves to derive gender-specific consumption cutoffs that maximized sensitivity and specificity in the prediction of a wide range of negative consequences from drinking. METHODS: We conducted secondary data analyses using data from two large clinical trials targeting alcohol use disorders: Project MATCH (n=1726) and COMBINE (n=1383). RESULTS: In both studies, we found that the ideal cutoff for men and women that maximized sensitivity/specificity varied substantially both across different alcohol consumption variables and alcohol consequence outcomes. Further, the levels of sensitivity/specificity were poor across all consequences. CONCLUSIONS: These results fail to provide support for a clinically meaningful alcohol consumption cutoff and suggest that binary classification of levels of alcohol consumption is a poor proxy for maximizing sensitivity/specificity in the prediction of negative consequences from drinking. Future research examining consumption-consequence associations should take advantage of continuous measures of alcohol consumption and alternative approaches for assessing the link between levels of consumption and consequences (e.g., ecological momentary assessment). Clinical researchers should consider focusing more directly on the consequences they aim to reduce instead of relying on consumption as a proxy for more clinically meaningful outcomes.
Authors: Raymond F Anton; Stephanie S O'Malley; Domenic A Ciraulo; Ron A Cisler; David Couper; Dennis M Donovan; David R Gastfriend; James D Hosking; Bankole A Johnson; Joseph S LoCastro; Richard Longabaugh; Barbara J Mason; Margaret E Mattson; William R Miller; Helen M Pettinati; Carrie L Randall; Robert Swift; Roger D Weiss; Lauren D Williams; Allen Zweben Journal: JAMA Date: 2006-05-03 Impact factor: 56.272
Authors: Vanessa A Jimenez; Nicole A R Walter; Tatiana A Shnitko; Natali Newman; Kaya Diem; Lauren Vanderhooft; Hazel Hunt; Kathleen A Grant Journal: J Pharmacol Exp Ther Date: 2020-09-01 Impact factor: 4.030