A reference-free R-learner for treatment recommendation

Junyi Zhou, Ying Zhang, Wanzhu Tu

Research output: Contribution to journalArticlepeer-review


Assigning optimal treatments to individual patients based on their characteristics is the ultimate goal of precision medicine. Deriving evidence-based recommendations from observational data while considering the causal treatment effects and patient heterogeneity is a challenging task, especially in situations of multiple treatment options. Herein, we propose a reference-free R-learner based on a simplex algorithm for treatment recommendation. We showed through extensive simulation that the proposed method produced accurate recommendations that corresponded to optimal treatment outcomes, regardless of the reference group. We used the method to analyze data from the Systolic Blood Pressure Intervention Trial (SPRINT) and achieved recommendations consistent with the current clinical guidelines.

Original languageEnglish (US)
Pages (from-to)404-424
Number of pages21
JournalStatistical Methods in Medical Research
Issue number2
StatePublished - Feb 2023


  • Heterogeneous treatment effect
  • R-learner
  • simplex
  • treatment recommendation

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


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