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Objective: The Alcohol Addiction Research Domain Criteria (AARDoC) is an organizational framework for assessing heterogeneity in addictive disorders organized across the addiction cycle domains of incentive salience, negative emotionality, and executive functioning and may have benefits for precision medicine. Recent work found pretreatment self-report items mapped onto the addiction cycle domains and predicted 1- and 3-year alcohol use disorder treatment outcomes. Given the potential utility of the addiction cycle domains for predicting relevant treatment outcomes, this study sought to evaluate the longitudinal measurement invariance of the domains. Method: We conducted a secondary analysis of individuals with alcohol use disorder (n = 1,383, 30.9% female, 76.8% non-Hispanic White, 11.2% Hispanic) who participated in the COMBINE study. Eleven items assessed at pre- and posttreatment were included in exploratory structural equation modeling (ESEM) and longitudinal invariance analyses. Results: The pre- and posttreatment ESEM models had factor loadings consistent with the three addiction cycle domains and fit the data well. The ESEM factor structure was invariant from pre- to posttreatment (representing configural invariance) and metric invariance (factor loadings) was largely supported, but analyses failed to support scalar invariance (item-level thresholds) of the addiction cycle domains. Conclusions: A three-factor structure representing addiction cycle domains can be modeled using brief self-report measures pre- and posttreatment. Individuals demonstrated a downward shift in the level of item endorsement, indicating improvement with treatment. Although this 11-item measure might be useful at baseline for informing treatment decisions, results indicate the need to exercise caution in comparing the addiction cycle domains pre- to posttreatment within persons. Public Health Significance Statement: The addiction cycle domains of reward/incentive salience, relief/negative emotionality, and executive functioning, which are based on a neurobiological model of alcohol use disorder, may have clinical utility in predicting alcohol use disorder treatment response and recovery outcomes, but more work is needed before domain scores can be used to assess changes within individuals across time. [ABSTRACT FROM AUTHOR]

Behavioral coding is increasingly used for studying mechanisms of change in psychosocial treatments for substance use disorders (SUDs). However, behavioral coding data typically include features that can be problematic in regression analyses, including measurement error in independent variables, non normal distributions of count outcome variables, and conflation of predictor and outcome variables with third variables, such as session length. Methodological research in econometrics has shown that these issues can lead to biased parameter estimates, inaccurate standard errors, and increased Type I and Type II error rates, yet these statistical issues are not widely known within SUD treatment research, or more generally, within psychotherapy coding research. Using minimally technical language intended for a broad audience of SUD treatment researchers, the present paper illustrates the nature in which these data issues are problematic. We draw on real-world data and simulation-based examples to illustrate how these data features can bias estimation of parameters and interpretation of models. A weighted negative binomial regression is introduced as an alternative to ordinary linear regression that appropriately addresses the data characteristics common to SUD treatment behavioral coding data. We conclude by demonstrating how to use and interpret these models with data from a study of motivational interviewing. SPSS and R syntax for weighted negative binomial regression models is included in online supplemental materials.

Objective: The addiction cycle has been proposed as a framework for understanding the progression of alcohol use disorder (AUD) in terms of psychological and biological domains, including reward drinking/incentive salience, relief drinking/negative emotionality, and loss of control/executive functioning impairment. To have utility in clinical practice, self-report measures of these domains that are applicable across sociodemographic groups and associated with clinical outcomes are needed. This study sought to validate domains from self-report measures and to test whether domains are measurement invariant across sociodemographic groups and associated with treatment outcomes. Method: Secondary analysis of individuals with AUD (n = 3,092) who participated in two alcohol clinical trials, Project Matching Alcohol Treatment to Client Heterogeneity (MATCH) and COMBINE. Factor analytic methods were used to derive addiction cycle domains at baseline. These domains were then examined as predictors of outcomes. Results: Fifteen self-report items were used as indicators of the addiction cycle domains, with sociodemographic differences in measurement by sex, age, race, education, and AUD symptoms. Relief/negative emotionality and reward/incentive salience were significantly associated with outcomes at 1 and 3 years following treatment, and executive functioning also predicted nonabstinent recovery at 3 years. Conclusions: The results support the utility of domains relevant to the addiction cycle in predicting AUD treatment outcomes and recovery among individuals who sought treatment for AUD. The addiction cycle domains were more strongly associated with outcomes than other measures clinicians might use to predict outcomes (e.g., AUD symptoms). Future research should continue to develop and refine the items and test whether the addiction cycle domains can inform treatment planning. [ABSTRACT FROM AUTHOR]