Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data

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 confounding bias is minimized to the extent possible. However, experimental studies are limited in external validity because of their selection restrictions, and therefore the underlying study population is 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 target 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. Theoretically, we establish the risk consistency for the proposed ITR estimator. Empirically, we evaluate the finite-sample performance of the transfer learner through simulations and apply it to a real data application of a job training program. Supplementary materials for this article are available online.

个体化治疗效应（individualized treatment effect）是精准医学的核心所在。可解释个体化治疗规则（Interpretable Individualized Treatment Rules, ITRs）凭借其直观性与透明度，深受临床医师与政策制定者的青睐。估计ITRs的金标准方法为随机试验：研究对象被随机分配至不同治疗组，混杂偏倚得以尽可能最小化。然而，试验研究因存在选择限制而受限于外部有效性，其纳入的研究人群无法代表目标真实世界人群。仅基于试验数据构建目标人群最优可解释ITRs的传统学习方法存在偏倚。另一方面，真实世界数据（Real-World Data, RWD）日益普及，可提供目标人群的代表性样本。为学习具有泛化性的最优可解释ITRs，本文提出一种基于加权方案的集成迁移学习方法，用于校准试验数据与RWD的协变量分布。理论层面，本文证明了所提ITRs估计量的风险一致性。实证层面，本文通过模拟实验评估了该迁移学习器的有限样本性能，并将其应用于一项职业培训项目的真实数据分析中。本文的补充材料可在线获取。