Random forests of interaction trees for estimating individualized treatment effects in randomized trials

Area of Science:

• Biostatistics
• Computational Biology
• Precision Medicine

Background:

• Assessing heterogeneous treatment effects is crucial for advancing precision medicine.
• Individualized treatment effects (ITEs) are key to tailoring medical interventions.
• Estimating ITEs from randomized trial data presents analytical challenges.

Purpose of the Study:

• To introduce a novel method, Random Forests of Interaction Trees (RFIT), for estimating ITEs.
• To enhance the efficiency of tree construction in ITE estimation.
• To provide a robust method for analyzing individualized treatment effects in clinical research.

Main Methods:

• Developed the Random Forests of Interaction Trees (RFIT) method based on interaction trees.
• Proposed a smooth sigmoid surrogate method to accelerate tree construction, replacing greedy search.
• Utilized the infinitesimal jackknife method for calculating standard errors of estimated ITEs.

Main Results:

• RFIT demonstrated superior performance in estimating ITEs compared to the "separate regression" approach.
• The proposed smooth sigmoid surrogate method effectively speeds up tree construction.
• Standard errors for RFIT-estimated ITEs were successfully obtained.

Conclusions:

• RFIT is a powerful and efficient method for estimating individualized treatment effects from randomized trials.
• The method has practical applications in precision medicine and clinical data analysis.
• RFIT provides a reliable tool for understanding treatment heterogeneity.