Meta-Analysis of individualized treatment rules via sign-coherency

Jay Jojo Cheng, Jared D. Huling, Guanhua Chen

Research output: Contribution to journalConference articlepeer-review


Medical treatments tailored to a patient's baseline characteristics hold the potential of improving patient outcomes while reducing negative side effects. Learning individualized treatment rules (ITRs) often requires aggregation of multiple datasets(sites); however, current ITR methodology does not take between-site heterogeneity into account, which can hurt model generalizability when deploying back to each site. To address this problem, we develop a method for individual-level metaanalysis of ITRs, which jointly learns site-specific ITRs while borrowing information about feature sign-coherency via a scientifically-motivated directionality principle. We also develop an adaptive procedure for model tuning, using information criteria tailored to the ITR learning problem. We study the proposed methods through numerical experiments to understand their performance under different levels of between-site heterogeneity and apply the methodology to estimate ITRs in a large multi-center database of electronic health records. This work extends several popular methodologies for estimating ITRs (A-learning, weighted learning) to the multiple-sites setting.

Original languageEnglish (US)
Pages (from-to)171-198
Number of pages28
JournalProceedings of Machine Learning Research
StatePublished - 2022
Event2nd Machine Learning for Health Symposium, ML4H 2022 - Hybrid, New Orleans, United States
Duration: Nov 28 2022 → …

Bibliographical note

Publisher Copyright:
© 2022 P.N. Argaw, E. Healey & I.S. Kohane.


  • Causal inference
  • Individualized treatment rule
  • Metaanalysis
  • Personalized medicine


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