An individualized treatment regime (ITR) is a decision rule that assigns treatments based on patients' characteristics. The value function of an ITR is the expected outcome in a counterfactual world had this ITR been implemented. Recently, there has been increasing interest in combining heterogeneous data sources, such as leveraging the complementary features of randomized controlled trial (RCT) data and a large observational study (OS). Usually, a covariate shift exists between the source and target population, rendering the source-optimal ITR unnecessarily optimal for the target population. We present an efficient and robust transfer learning framework for estimating the optimal ITR with right-censored survival data that generalizes well to the target population. The value function accommodates a broad class of functionals of survival distributions, including survival probabilities and restrictive mean survival times (RMSTs). We propose a doubly robust estimator of the value function, and the optimal ITR is learned by maximizing the value function within a pre-specified class of ITRs. We establish the $N^{-1/3}$ rate of convergence for the estimated parameter indexing the optimal ITR, and show that the proposed optimal value estimator is consistent and asymptotically normal even with flexible machine learning methods for nuisance parameter estimation. We evaluate the empirical performance of the proposed method by simulation studies and a real data application of sodium bicarbonate therapy for patients with severe metabolic acidaemia in the intensive care unit (ICU), combining a RCT and an observational study with heterogeneity.

翻译：个人化治疗制度(IRT)是一种基于患者特征的治疗分配决策规则。 ITR的价值功能是一个反事实世界的预期结果。最近,人们越来越有兴趣将多种数据来源结合起来,例如利用随机控制的试验(RCT)数据和大规模观测研究(OS)的互补特征。通常,源和目标人口之间存在一种混合变化,使源的最佳ITR对目标人口来说不必要地最优化。我们提出了一个高效和有力的传输学习框架,用以估计最佳ITR,并配有正确测试的存活数据,对目标人口加以概括。 最近,人们越来越有兴趣将各种数据来源结合起来,例如利用随机控制的试验(RCT)数据和大规模观察研究(OSO)数据的互补功能。我们提出了一个更强有力的价值功能估计,使ITR对目标人口来说是最佳的。我们提出了一个高效和有力的传输学习框架学习框架,通过一个预先指定的分类,使ITR(ITR)类别内的拟议数值最大化。我们为估计的精确和精确的RTR数据结合率制定了美元比率,将ITR数据与最佳的正常的指数化分析方法结合起来,并显示最佳的ITR数据与最佳的指数化分析方法。我们提议的研算的精确的研算方法的研估的精确的数值比值比值比值比值比值评估。