Learning individualized treatment rules (ITRs) is an important topic in precision medicine. Current literature mainly focuses on deriving ITRs from a single source population. We consider the observational data setting when the source population differs from a target population of interest. We assume subject covariates are available from both populations, but treatment and outcome data are only available from the source population. Although adjusting for differences between source and target populations can potentially lead to an improved ITR for the target population, it can substantially increase the variability in ITR estimation. To address this dilemma, we develop a weighting framework that aims to tailor an ITR for a given target population and protect against high variability due to superfluous covariate shift adjustments. Our method seeks covariate balance over a nonparametric function class characterized by a reproducing kernel Hilbert space and can improve many ITR learning methods that rely on weights. We show that the proposed method encompasses importance weights and the so-called overlap weights as two extreme cases, allowing for a better bias-variance trade-off in between. Numerical examples demonstrate that the use of our weighting method can greatly improve ITR estimation for the target population compared with other weighting methods.
翻译:在精密医学中,学习个人化治疗规则(ITRs)是一个重要的专题。当前文献主要侧重于从单一源人口中得出ITRs。当源人口与感兴趣的目标人口不同时,我们考虑观察数据设置。我们假定这两个人口都有主题共变,但治疗和结果数据只能从源人口获得。尽管根据源和目标人口之间的差异进行调整,可能会导致目标人口ITR的改进,但可以大大增加ITR估计的变异性。为解决这一难题,我们制定了一个加权框架,旨在为特定目标人口定制ITR,并防范因过度的共变换变化调整而导致的高度变异性。我们的方法寻求在非对等功能类别上实现平衡,该类的特征是再生内核Hilbert空间,可以改进许多依赖重量的ITR学习方法。我们表明,拟议方法包括重要性加权和所谓的重叠权重,作为两个极端案例,可以使两者之间的偏差性权衡得更好。数字实例表明,使用我们的加权方法可以大大改进目标人口ITR的加权比其他方法。