PDF(Pubmed)
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 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.
摘要：
个体化治疗效果是精准医学的核心。由于其直观的吸引力和透明度，可解释的个性化治疗规则（ITR）对于临床医生或决策者来说是可取的。估计ITR的金标准方法是随机实验，其中受试者被随机分配到不同的治疗组，并且混淆偏差尽可能最小化。然而,实验研究由于其选择限制而受到外部有效性的限制，因此，基础研究人群不能代表目标现实世界人群。仅基于实验数据的目标人群的最佳可解释ITR的常规学习方法是有偏差的。另一方面,现实世界数据(RWD)正在变得流行，并提供了目标人群的代表性样本。要学习可推广的最佳可解释ITR，我们提出了一种基于加权方案的综合迁移学习方法，以将实验的协变量分布校准为RWD的协变量分布。理论上,我们为拟议的ITR估计器建立了风险一致性。根据经验,我们通过模拟来评估迁移学习者的有限样本性能，并将其应用于作业培训计划的实际数据应用。
