title: Subgroup Identification in Personalized Treatment of Alcohol Dependence.
abstract: BACKGROUND
  Identification of patient subgroups to enhance treatment effects is an important topic in personalized (or tailored) alcohol treatment. Recently, several recursive partitioning methods have been proposed to identify subgroups benefiting from treatment. These novel data mining methods help to address the limitations of traditional regression-based methods that focus on interactions.
  
  
  METHODS
  We propose an exploratory approach, using recursive partitioning methods, for example, interaction trees (IT) and virtual twins (VT), to flexibly identify subgroups in which the treatment effect is likely to be large. We apply these tree-based methods to a pharmacogenetic trial of ondansetron.
  
  
  RESULTS
  Our methods identified several subgroups based on patients' genetic and other prognostic covariates. Among the 251 subjects with complete genotype information, the IT method identified 118 with specific genetic and other prognostic factors, resulting in a 17.2% decrease in the percentage of heavy drinking days (PHDD). The VT method identified 88 subjects with a 21.8% decrease in PHDD. Overall, the VT subgroup achieved a good balance between the treatment effect and the group size.
  
  
  CONCLUSIONS
  A data mining approach is proposed as a valid exploratory method to identify a sufficiently large subgroup of subjects that is likely to receive benefit from treatment in an alcohol dependence pharmacotherapy trial. Our results provide new insights into the heterogeneous nature of alcohol dependence and could help clinicians to tailor treatment to the biological profile of individual patients, thereby achieving better treatment outcomes.
authors.affiliation: Department of Statistics, University of Illinois, Urbana, Illinois.
authors.forename: Jue
authors.initials: J
authors.lastname: Hou
authors.affiliation: Department of Psychiatry, Institute for Genome Sciences, University of Maryland, Baltimore, Maryland.
authors.forename: Chamindi
authors.initials: C
authors.lastname: Seneviratne
authors.affiliation: Department of Mathematical Sciences, University of Texas at El Paso (UTEP), El Paso, Texas.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Su
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Taylor
authors.affiliation: Department of Psychiatry, Institute for Genome Sciences, University of Maryland, Baltimore, Maryland.
authors.forename: Bankole
authors.initials: B
authors.lastname: Johnson
authors.affiliation: Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia.
authors.forename: Xin-Qun
authors.initials: XQ
authors.lastname: Wang
authors.affiliation: Department of Biostatistics, Yale University, New Haven, Connecticut.
authors.forename: Heping
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania.
authors.forename: Henry R
authors.initials: HR
authors.lastname: Kranzler
authors.affiliation: Department of Preventive Medicine, Northwestern University, Chicago, Illinois.
authors.forename: Joseph
authors.initials: J
authors.lastname: Kang
authors.affiliation: Department of Preventive Medicine, Northwestern University, Chicago, Illinois.
authors.forename: Lei
authors.initials: L
authors.lastname: Liu
mesh_terms: D000328:Adult; D000368:Aged; D000437:Alcoholism / Q000188:drug therapy*; D006801:Humans; D008875:Middle Aged; D017294:Ondansetron / Q000627:therapeutic use*; D010597:Pharmacogenetics; D057285:Precision Medicine; D012702:Serotonin Antagonists / Q000627:therapeutic use*; D050486:Serotonin Plasma Membrane Transport Proteins / Q000235:genetics*; D055815:Young Adult
chemical_list: C493230:SLC6A4 protein, human; D012702:Serotonin Antagonists; D050486:Serotonin Plasma Membrane Transport Proteins; D017294:Ondansetron
keywords: Alcohol Research; Classification and Regression Tree; Clinical Trial; Random Forest