title: Trajectory analyses in alcohol treatment research.
abstract: BACKGROUND
  Various statistical methods have been used for data analysis in alcohol treatment studies. Trajectory analyses can better capture differences in treatment effects and may provide insight on the optimal duration of future clinical trials and grace periods. This improves on the limitation of commonly used parametric (e.g., linear) methods that cannot capture nonlinear temporal trends in the data.
  
  
  METHODS
  We propose an exploratory approach, using more flexible smoothing mixed effects models, more accurately to characterize the temporal patterns of the drinking data. We estimated the trajectories of the treatment arms for data sets from 2 sources: a multisite topiramate study, and the Combined Pharmacotherapies (acamprosate and naltrexone) and Behavioral Interventions study.
  
  
  RESULTS
  Our methods illustrate that drinking outcomes of both the topiramate and placebo arms declined over the entire course of the trial but with a greater rate of decline for the topiramate arm. By the point-wise confidence intervals, the heavy drinking probabilities for the topiramate arm might differ from those of the placebo arm as early as week 2. Furthermore, the heavy drinking probabilities of both arms seemed to stabilize at the end of the study. Overall, naltrexone was better than placebo in reducing drinking over time yet was not different from placebo for subjects receiving the combination of a brief medical management and an intensive combined behavioral intervention.
  
  
  CONCLUSIONS
  The estimated trajectory plots clearly showed nonlinear temporal trends of the treatment with different medications on drinking outcomes and offered more detailed interpretation of the results. This trajectory analysis approach is proposed as a valid exploratory method for evaluating efficacy in pharmacotherapy trials in alcoholism.
authors.affiliation: Department of Preventive Medicine, Northwestern University, Chicago, Illinois, USA.
authors.forename: Jinsong
authors.initials: J
authors.lastname: Chen
authors.forename: Bankole A
authors.initials: BA
authors.lastname: Johnson
authors.forename: Xin-Qun
authors.initials: XQ
authors.lastname: Wang
authors.forename: John
authors.initials: J
authors.lastname: O'Quigley
authors.forename: Maria
authors.initials: M
authors.lastname: Isaac
authors.forename: Daowen
authors.initials: D
authors.lastname: Zhang
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
mesh_terms: D000077443:Acamprosate; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000427:Alcohol Deterrents / Q000627:therapeutic use; D000437:Alcoholism / Q000188:drug therapy / Q000628:therapy*; D000465:Algorithms; D001521:Behavior Therapy; D003131:Combined Modality Therapy; D003627:Data Interpretation, Statistical; D004311:Double-Blind Method; D005260:Female; D005632:Fructose / Q000031:analogs & derivatives / Q000627:therapeutic use; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009271:Naltrexone / Q000627:therapeutic use; D009292:Narcotic Antagonists / Q000627:therapeutic use; D018696:Neuroprotective Agents / Q000627:therapeutic use; D017711:Nonlinear Dynamics; D012044:Regression Analysis; D012106:Research / Q000706:statistics & numerical data*; D012107:Research Design; D013654:Taurine / Q000031:analogs & derivatives / Q000627:therapeutic use; D000077236:Topiramate; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D000427:Alcohol Deterrents; D009292:Narcotic Antagonists; D018696:Neuroprotective Agents; D000077236:Topiramate; D013654:Taurine; D005632:Fructose; D009271:Naltrexone; D000077443:Acamprosate