Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups

Stat Med. 2019 Jun 30;38(14):2632-2651. doi: 10.1002/sim.8105. Epub 2019 Mar 19.


Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. In clinical practices, it is desirable to derive a simple and interpretable ITR while maintaining certain optimality that leads to improved benefit in subgroups of patients, if not on the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piecewise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy trial for treating patients with major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit.

Keywords: mental disorders; personalized medicine; qualitative interaction; quantitative interaction; tree-based method.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Female
  • Humans
  • Learning*
  • Machine Learning
  • Male
  • Mental Disorders / therapy
  • Middle Aged
  • Precision Medicine*
  • Psychotherapy
  • Qualitative Research
  • Randomized Controlled Trials as Topic / statistics & numerical data