Subgroup Identification in Personalized Treatment of Alcohol Dependence

Jue Hou, Chamindi Seneviratne, Xiaogang Su, Jeremy Taylor, Bankole Johnson, Xin Qun Wang, Heping Zhang, Henry R. Kranzler, Joseph Kang, Lei Liu

Research output: Contribution to journalArticle

10 Scopus citations

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.

Original languageEnglish
Pages (from-to)1253-1259
Number of pages7
JournalAlcoholism: Clinical and Experimental Research
Volume39
Issue number7
DOIs
StatePublished - Jul 1 2015
Externally publishedYes

Keywords

  • Alcohol Research
  • Classification and Regression Tree
  • Clinical Trial
  • Random Forest

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  • Cite this

    Hou, J., Seneviratne, C., Su, X., Taylor, J., Johnson, B., Wang, X. Q., Zhang, H., Kranzler, H. R., Kang, J., & Liu, L. (2015). Subgroup Identification in Personalized Treatment of Alcohol Dependence. Alcoholism: Clinical and Experimental Research, 39(7), 1253-1259. https://doi.org/10.1111/acer.12759