Statistics and Its Interface

Volume 9 (2016)

Number 2

A modified classification tree method for personalized medicine decisions

Pages: 239 – 253

DOI: http://dx.doi.org/10.4310/SII.2016.v9.n2.a11

Authors

Wan-Min Tsai (Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, U.S.A.)

Heping Zhang (Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, U.S.A.)

Eugenia Buta (Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, U.S.A.)

Stephanie O’Malley (Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, U.S.A.)

Ralitza Gueorguieva (Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, U.S.A.; and Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, U.S.A.)

Abstract

The tree-based methodology has been widely applied to identify predictors of health outcomes in medical studies. However, the classical tree-based approaches do not pay particular attention to treatment assignment and thus do not consider prediction in the context of treatment received. In recent years, attention has been shifting from average treatment effects to identifying moderators of treatment response, and tree-based approaches to identify subgroups of subjects with enhanced treatment responses are emerging. In this study, we extend and present modifications to one of these approaches (Zhang et al., 2010) to efficiently identify subgroups of subjects who respond more favorably to one treatment than another based on their baseline characteristics. We extend the algorithm by incorporating an automatic pruning step and propose a measure for assessment of the predictive performance of the constructed tree. We evaluate the proposed method through a simulation study and illustrate the approach using a data set from a clinical trial of treatments for alcohol dependence. This simple and efficient statistical tool can be used for developing algorithms for clinical decision making and personalized treatment for patients based on their characteristics.

Keywords

binary tree, classification tree, decision tree, recursive partitioning, subgroup, personalized medicine, tailored treatment

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