Reluctant Transfer Learning in Penalized Regressions for Individualized Treatment Rules Under Effect Heterogeneity

Area of Science:

• Biostatistics
• Machine Learning
• Precision Medicine

Background:

• Estimating individualized treatment rules (ITRs) is crucial for precision medicine, tailoring treatments to patient characteristics.
• Existing ITR methods lack robust model updating strategies for shifted treatment-covariate relationships in new datasets.
• Adapting models to evolving treatment effects is essential for real-world clinical applications.

Purpose of the Study:

• To propose a novel reluctant transfer learning (RTL) framework for efficient ITR model adaptation to target datasets with treatment effect shifts.
• To enable model updating without requiring access to individual-level source data, addressing privacy and logistical constraints.
• To develop a method that selectively transfers model components, controls complexity, and enhances generalizability.

Main Methods:

• Developed a reluctant transfer learning (RTL) framework for ITR estimation.
• Implemented selective transfer of model components (e.g., regression coefficients) from source to target data.
• Incorporated model adjustments based on performance improvements on the target dataset, ensuring reluctant modeling.
• Supported multi-armed treatment settings and performed variable selection for interpretability.
• Provided a regret bound to quantify the performance of the estimated ITR.

Main Results:

• The RTL framework demonstrated efficient model adaptation to target datasets with shifted treatment effects.
• Simulations and a real-world data application (BestAIR trial) showed RTL outperformed existing methods.
• The method proved effective in multi-armed settings and offered interpretable variable selection.
• A regret bound was established, providing theoretical guarantees on the estimated ITR's value.

Conclusions:

• The proposed RTL framework offers an efficient and practical solution for adaptive treatment decision-making under evolving treatment effect conditions.
• RTL enhances the generalizability and robustness of ITR models in precision medicine.
• This approach facilitates the deployment of adaptive treatment strategies in dynamic clinical environments.