Multicategory individualized treatment regime using outcome weighted learning

Xinyang Huang, Yair Goldberg, Jin Xu

Research output: Contribution to journalArticlepeer-review


Individualized treatment regimes (ITRs) aim to recommend treatments based on patient-specific characteristics in order to maximize the expected clinical outcome. Outcome weighted learning approaches have been proposed for this optimization problem with primary focus on the binary treatment case. Many require assumptions of the outcome value or the randomization mechanism. In this paper, we propose a general framework for multicategory ITRs using generic surrogate risk. The proposed method accommodates the situations when the outcome takes negative value and/or when the propensity score is unknown. Theoretical results about Fisher consistency, excess risk, and risk consistency are established. In practice, we recommend using differentiable convex loss for computational optimization. We demonstrate the superiority of the proposed method under multinomial deviance risk to some existing methods by simulation and application on data from a clinical trial.

Original languageEnglish
Pages (from-to)1216-1227
Number of pages12
Issue number4
StatePublished - 1 Dec 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 The International Biometric Society


  • individualized treatment regime
  • multicategory classification
  • multinomial deviance
  • outcome weighted learning
  • personalized medicine

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • Applied Mathematics
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • Statistics and Probability


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