Transfer learning of individualized treatment rules from experimental to real-world data

TL;DR

This paper introduces a transfer learning approach to develop interpretable individualized treatment rules that generalize from experimental data to real-world data, improving external validity in precision medicine.

Contribution

It proposes a novel weighting-based transfer learning method for ITRs that combines experimental and real-world data, with theoretical risk guarantees.

Findings

The method achieves risk consistency theoretically.

Simulation studies show improved performance over traditional methods.

Application to a job training program demonstrates practical utility.

Abstract

Individualized treatment effect lies at the heart of precision medicine. Interpretable individualized treatment rules (ITRs) are desirable for clinicians or policymakers due to their intuitive appeal and transparency. The gold-standard approach to estimating the ITRs is randomized experiments, where subjects are randomized to different treatment groups and the bias is minimized to the extent possible. However, experimental data are limited in external validity because of their selection restrictions and therefore are not representative of the target real-world population. Conventional learning methods of optimal interpretable ITRs for a target population based only on experimental data are biased. On the other hand, real-world data (RWD) are becoming popular and provide a representative sample of the population. To learn the generalizable optimal interpretable ITRs, we propose an integrative transfer learning method based on weighting schemes to calibrate the covariate distribution of the experiment to that of the RWD. We show that the proposed ITR estimator has a theoretical guarantee of the risk consistency. We evaluate the transfer learner based on the finite-sample performance through simulation and apply it to a real data application of a job training program.