• Alcohol Treatment;
  • Relapse;
  • Growth Models;
  • Mixture Models;
  • Heterogeneity

Background:  The ultimate goal of alcohol treatment research is to develop interventions that help individuals reduce their alcohol use. To determine whether a treatment is effective, researchers must then evaluate whether a particular treatment affects changes in drinking behavior after treatment. Importantly, drinking following treatment tends to be highly variable between individuals and within individuals across time.

Method:  Using data from the COMBINE study (COMBINE Study Group, 2003), the current study compared 3 commonly used and novel methods for analyzing changes in drinking over time: latent growth curve (LGC) analysis, growth mixture models, and latent Markov models. Specifically, using self-reported drinking data from all participants (= 1,383, 69% male), we were interested in examining how well the 3 estimated models were able to explain observed changes in percent heavy drinking days during the 52 weeks following treatment.

Results:  The results from all 3 models indicated that the majority of individuals were either abstinent or reported few heavy drinking days during the 52-week follow-up and only a minority of individuals (10% or fewer) reported consistently frequent heavy drinking following treatment. All 3 models provided a reasonably good fit to the observed data with the latent Markov models providing the closest fit. The observed drinking trajectories evinced discontinuity, whereby individuals seem to transition between drinking and nondrinking across adjacent follow-up assessment points. The LGC and growth mixture models both assumed continuous change and could not explain this discontinuity in the observed drinking trajectories, whereas the latent Markov approach explicitly modeled transitions between drinking states.

Conclusions:  The 3 models tested in the current study provided a unique look at the observed drinking among individuals who received treatment for alcohol dependence. Latent Markov modeling may be a highly desirable methodology for gaining a better sense of transitions between positive and negative drinking outcomes.