Integrative Data Analysis to Predict Alcohol Clinical Course and Inform Practice, K. Witkiewitz (PI).
In spite of the adoption of many evidence-based behavioral and pharmacological interventions by alcohol treatment providers it is still common for drinking to occur after treatment, frequently at problematic levels. Very little is known, however, about who is most at risk for heavy drinking relapses, under what conditions non-problematic drinking escalates to pre-treatment levels of drinking or, conversely, heavy drinking is reduced after an initial lapse, and when non-problematic drinking resolves without the need for additional intervention efforts. In general, the mechanisms of change following alcohol treatment are not well understood. To address these gaps in understanding, this study will build an empirical knowledge base of predictors of successful alcohol treatment outcomes among individuals who receive alcohol treatment. The achievement of study aims will provide clinicians with both normative-based guidelines to identify "at risk" individuals during treatment as well as criteria to evaluate the need for additional treatment when posttreatment drinking occurs. To develop these guidelines and criteria, integrative data analysis of factors influencing alcohol clinical course among more than 4,000 individuals (n = 4,415) who participated in four publicly funded alcohol treatment studies will be conducted. In order to characterize individual drinking norms this study will consider the complexities of alcohol clinical course by drawing on a dynamic theoretical model of relapse that emphasizes the interaction between predispositions and risk factors in the prediction of alcohol clinical course. The first aim of this research is to examine individual patterns of drinking and the probabilities of lapsing at various time points during and following treatment, as well as the probabilities of transitioning from a lapse to relapse. Using these probabilities of drinking as the basis for defining unsuccessful and successful treatment outcomes, this research aims to examine the degree to which individual characteristics and treatment factors interact with individual behaviors and environmental factors in the prediction of alcohol treatment outcomes. The results from this study will be directly applicable to clinical practice by providing guidance on the individual characteristics, precipitating events, and treatment modifiable factors that are associated with changes in drinking patterns during treatment and one year following treatment. These data will allow providers to predict client outcomes during and after treatment and will offer practical strategies regarding steps that could reduce the likelihood of a return to heavy drinking. The results will also help clinicians identify those individual characteristics and environmental factors that predict a higher probability of maintaining a low-risk drinking trajectory. Moreover, the probabilities derived from the research can be used for the development of an empirically-based clinical decision making support system. Such a system, unprecedented in the alcohol treatment field, could be widely disseminated to clinicians, program evaluators, and policy makers in order to help improve alcohol treatment decision-making and improve treatment outcomes.