Lapses following Alcohol Treatment: Modeling the Falls From the Wagon

Abstract

Objective-This study investigated transitions between drinking and nondrinking during the first 12 months following treatment and whether transitions in posttreatment drinking are related to alcohol-dependence symptoms.Method-Data from individuals in the outpatient (n = 952) and aftercare (n = 774) arms of Project MATCH (Matching Alcoholism Treatments to Client Heterogeneity) were included in the analyses. Drinking consequences, percentage of drinking days, and drinks per drinking day were used as indicators of drinking behavior. Latent transition analysis was used to estimate a model of drinking patterns, defined by transition probabilities between drinking classes, from immediately following treatment to 6 and 12 months following treatment.Results-Across both aftercare and outpatient samples, three drinking classes were identified at each time point: frequent heavy drinking with high consequences, moderate infrequent drinking with low consequences, and nondrinking with low consequences. Many participants maintained nondrinking, and, of those who drank, there was a trend toward transitioning to less drinking over time. Transition probabilities were noninvariant across treatment arms: The probability of transitioning from moderate drinking to frequent drinking was more than six times more likely in the aftercare arm, as compared with the outpatient arm. In both samples the transition to heavy drinking and membership in the heavy-drinking class were significantly positively related to alcoholdependence symptoms. There were no differences across MATCH treatment groups.Conclusions-This study examined transitions in post-treatment drinking and the role of alcohol dependence in predicting posttreatment drinking. The results suggest a low probability of moderate drinking among individuals with greater alcohol dependence.

Temporal Stability of Heavy Drinking Days and Drinking Reductions Among Heavy Drinkers in the COMBINE Study

“…For all models, the BIC tended to decrease with each additional latent class and the entropy statistic indicated good classification precision across all models. Given substantive interpretation of the resulting class solutions and prior research (Witkiewitz, 2008; Witkiewitz et al, 2010), we selected the models with three latent classes as the final model within each month of treatment.…”

“…The models did differ on the invariance of the τ parameters, as reported in detail below. Based on prior work (Witkiewitz, 2008; Witkiewitz et al, 2010), we hypothesized that the probability of transitioning would be greatest during treatment and in the months following treatment, whereas the rates of transitioning would decrease at the 12-month follow-up when heavy drinking (or non-heavy drinking) states may become more stable. Covariates and treatment group were not considered in the analyses because we were most interested in studying the patterns of stability and change in heavy drinking across time, irrespective of treatment and other covariates.…”

Predictors of heavy drinking during and following treatment.

“…Five variables were selected as relatively baseline characteristics that have been previously been associated with alcohol relapse: self-efficacy (Witkiewitz, van der Maas, Hufford, & Marlatt, 2007); alcohol dependence severity (Witkiewitz, 2008), marital status (Dawson, Grant, Stinson, Chou, Boji, & Ruan, 2006), treatment history and psychiatric problems (Hufford, Witkiewitz, Shields, Kodva, & Caruso, 2003). Other risk factors were considered based on their availability in the COMBINE data, including readiness to change, social support, drinking network size, and family history of alcohol problems.…”

Aggregating and Analyzing Daily Drinking Data in Clinical Trials: A Comparison of Type I Errors, Power, and Bias

“…Not all models could include identical effects (e.g., no random slopes in GEE, no time effects in OLS). Recent studies have found that mixture models (i.e., representing unmeasured subgroups) and zero-inflated models (i.e., representing nondrinkers as a specific subgroup) improve model fit (Atkins et al, 2013;Witkiewitz, 2008;Witkiewitz et al, 2010); however, these models add computational complexity, require large samples, and are less commonly used in treatment research. Testing the above methods with missing data was, unfortunately, beyond the scope of the present study, and participants with missing data were excluded; therefore, the results should be interpreted as best-case scenarios if missing data are accounted for in an ideal manner.…”