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Non-parametric individual treatment effect estimation for survival data with random forests.

Sami Tabib1, Denis Larocque1

  • 1Department of Decision Sciences, HEC Montréal, Montréal, QC H3T 2A7, Canada.

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A new random forest method accurately estimates individual treatment effects for survival data, even with censoring. This approach shows strong performance in simulations and identifies treatment effects in cancer patient sub-populations.

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Personalized Medicine

Background:

  • Accurate estimation of individual treatment effects is crucial for personalized medicine.
  • Time-to-event data with censoring presents unique challenges for treatment effect estimation.
  • Existing methods may struggle to identify treatment effects in specific patient subgroups.

Purpose of the Study:

  • To propose a novel random forest method for estimating individual treatment effects with censored survival data.
  • To evaluate the performance of the proposed method against existing state-of-the-art techniques.
  • To demonstrate the method's utility in identifying treatment effects within specific subpopulations using real-world cancer data.

Main Methods:

  • Development of a random forest algorithm with a specialized splitting rule tailored for individual treatment effect estimation.
  • Utilizing a dataset of similar subjects from the training data to compute individual treatment effect estimations.
  • Validation through simulation studies comparing the proposed method with multiple competitors.

Main Results:

  • The proposed random forest method demonstrates robust and stable performance in estimating individual treatment effects.
  • Simulation results indicate superior or comparable performance against numerous competing methods.
  • Application to colon and breast cancer data successfully identified treatment effects in subpopulations, even when overall effects were negligible.

Conclusions:

  • The novel random forest approach provides a reliable tool for estimating individual treatment effects in the presence of censored survival data.
  • The method has the potential to uncover treatment benefits in specific patient subgroups, advancing personalized cancer therapy.
  • An R package for the method will be available soon, with code accessible upon request.