Robust sample weighting to facilitate individualized treatment rule learning for a target population

• 成果类型: Article
• 署名作者: Chen, Rui; Huling, Jared D.; Chen, Guanhua; Yu, Menggang
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; University of Minnesota System; University of Minnesota Twin Cities; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asad038
• 发表日期: 2024
• 关键词: subgroup identification; propensity score; generalizing evidence; TRIAL; probability; models

摘要

Learning individualized treatment rules is an important topic in precision medicine. Current literature mainly focuses on deriving individualized treatment rules from a single source population. We consider the observational data setting when the source population differs from a target population of interest. Compared with causal generalization for the average treatment effect that is a scalar quantity, individualized treatment rule generalization poses new challenges due to the need to model and generalize the rules based on a prespecified class of functions that may not contain the unrestricted true optimal individualized treatment rule. The aim of this paper is to develop a weighting framework to mitigate the impact of such misspecification, and thus facilitate the generalizability of optimal individualized treatment rules from a source population to a target population. Our method seeks covariate balance over a nonparametric function class characterized by a reproducing kernel Hilbert space and can improve many individualized treatment rule learning methods that rely on weights. We show that the proposed method encompasses importance weights and 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 individualized treatment rule estimation for the target population compared with other weighting methods.