TY - JOUR
T1 - Subgroup Identification in Personalized Treatment of Alcohol Dependence
AU - Hou, Jue
AU - Seneviratne, Chamindi
AU - Su, Xiaogang
AU - Taylor, Jeremy
AU - Johnson, Bankole
AU - Wang, Xin Qun
AU - Zhang, Heping
AU - Kranzler, Henry R.
AU - Kang, Joseph
AU - Liu, Lei
N1 - Publisher Copyright:
© 2015 by the Research Society on Alcoholism.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - 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.
AB - 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.
KW - Alcohol Research
KW - Classification and Regression Tree
KW - Clinical Trial
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=84934444508&partnerID=8YFLogxK
U2 - 10.1111/acer.12759
DO - 10.1111/acer.12759
M3 - Article
C2 - 26031187
AN - SCOPUS:84934444508
SN - 0145-6008
VL - 39
SP - 1253
EP - 1259
JO - Alcoholism: Clinical and Experimental Research
JF - Alcoholism: Clinical and Experimental Research
IS - 7
ER -