title: A comparison of methods for estimating change in drinking following alcohol treatment.
abstract: 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.
  
  
  METHODS
  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 (n = 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.
authors.affiliation: Alcohol and Drug Abuse Institute, University of Washington, Seattle, Washington, USA. katie.witkiewitz@wsu.edu
authors.forename: Katie
authors.initials: K
authors.lastname: Witkiewitz
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Maisto
authors.forename: Dennis M
authors.initials: DM
authors.lastname: Donovan
mesh_terms: D000328:Adult; D000427:Alcohol Deterrents / Q000627:therapeutic use*; D000428:Alcohol Drinking / Q000188:drug therapy*; D000437:Alcoholism / Q000188:drug therapy*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D057566:Self Report; D016896:Treatment Outcome
chemical_list: D000427:Alcohol Deterrents