Tree-based methods for individualized treatment regimes.

Area of Science:

• * Machine Learning and Causal Inference
• * Biostatistics and Clinical Decision Support

Background:

• * Individualized treatment rules (ITRs) aim to tailor medical interventions to patient characteristics for improved outcomes.
• * Interpretability of ITRs is crucial for clinical adoption and scientific advancement, yet often lacking in complex models.
• * Black-box models hinder clinician trust and limit their utility in guiding future research.

Purpose of the Study:

• * To propose a novel method for estimating optimal individualized treatment rules within the class of interpretable decision trees.
• * To address the unsupervised nature of learning optimal treatments when patient outcomes are unknown.
• * To develop a method applicable to both categorical and continuous treatment variables.

Main Methods:

• * Proposed a method for estimating optimal individualized treatment rules representable as decision trees.
• * Employed an unsupervised learning approach to estimate the best treatment for each patient.
• * The method is designed to be both interpretable and sufficiently expressive for complex treatment strategies.

Main Results:

• * Simulation experiments demonstrated favorable marginal mean outcomes using the proposed method.
• * The method successfully estimated optimal treatment rules within the specified interpretable class.
• * The approach was illustrated using real-world data from a major depressive disorder study.

Conclusions:

• * The developed decision tree-based method provides an interpretable yet expressive way to estimate optimal individualized treatment rules.
• * This approach can lead to better patient outcomes, reduced costs, and less treatment burden.
• * The method holds promise for informing clinical practice and advancing research in personalized medicine.