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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Random forests for individual treatment effect estimation with the R package ITERF.

Sami Tabib1, Denis Larocque1

  • 1Department of Decision Sciences, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montréal (Québec), Canada, H3T 2A7.

Computer Methods and Programs in Biomedicine
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

The ITERF R package estimates individual treatment effects using random forests, crucial for personalized medicine. It introduces new methods for maximum treatment effect estimation, showing promise in simulations and real-world data analysis.

Keywords:
Conditional average treatment effectContinuous treatmentHeterogeneous treatmentIndividual treatment effectMaximum treatment effectR packageRandom forestSurvival dataTree-based method

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Area of Science:

  • Statistical learning
  • Biostatistics
  • Personalized medicine

Background:

  • Individual treatment effects vary significantly within populations.
  • Accurate estimation of individual treatment effects is vital for personalized medicine.
  • Random forests are powerful statistical learning methods for complex data analysis.

Purpose of the Study:

  • Introduce the R package ITERF for estimating individual treatment effects using random forests.
  • Develop and present novel methods for estimating the maximum individual treatment effect.
  • Provide a tool for researchers and practitioners in personalized treatment evaluation.

Main Methods:

  • Utilizes random forests for treatment effect estimation.
  • Implements methods for survival outcomes with right-censoring and binary treatments.
  • Offers methods for continuous outcomes with continuous treatments.

Main Results:

  • A simulation study confirmed the proposed methods for maximum treatment effect estimation perform well.
  • The ITERF package demonstrates considerable promise for accurate treatment effect estimation.
  • Real-world data analysis explored the relationship between sleep duration and cognitive health in the elderly.

Conclusions:

  • The ITERF package is a fast, user-friendly tool for estimating treatment effects.
  • Leverages random forests for robust and efficient individual treatment effect analysis.
  • A valuable resource for advancing personalized treatment strategies and evaluations.