A unified approach for subgroup identification and individualized treatment recommendation with applications to randomized control trials and observational studies

Haoda Fu, Jin Zhou

Research output: Contribution to journalArticle

Abstract

Precision medicine is important in the new era of medical product development. It focuses on optimizing healthcare decision for each individual patient based on this subject's context information. Traditional statistics methods for precision medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Its has limited capability to handle observational studies where treatment assignments could depend on covariates. In this paper, we described the limitations of traditional subgroup identification methods, and propose a general framework which connects the subgroup identification methods and individualized treatment recommendation rules. The proposed framework is able to handle two or more than two treatments from both randomized control trials and observation studies. We implement our algorithm in C++, and connect it with R. The performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study.

Original languageEnglish (US)
Pages (from-to)287-301
Number of pages15
JournalModel Assisted Statistics and Applications
Volume12
Issue number3
DOIs
StatePublished - Jan 1 2017

Fingerprint

Observational Study
Medicine
Recommendations
Subgroup
Medical problems
Product development
Statistics
Diabetes
Product Development
C++
Healthcare
Covariates
Assignment
Simulation
Framework

Keywords

  • Multiple treatments
  • observational studies
  • personalized medicine
  • randomized control trials
  • subgroup identification
  • value function

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

Cite this

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