Treatment Effect Estimation Under Additive Hazards Models With High-Dimensional Confounding

• 成果类型: Article
• 署名作者: Hou, Jue; Bradic, Jelena; Xu, Ronghui
• 署名单位: Harvard University; Harvard T.H. Chan School of Public Health; University of California System; University of California San Diego; University of California System; University of California San Diego; University of California System; University of California San Diego
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1930546
• 发表日期: 2023
• 页码: 327-342
• 关键词: regularized calibrated estimation; Semiparametric Efficiency; prostate-cancer; inference; selection

摘要

Estimating treatment effects for survival outcomes in the high-dimensional setting is critical for many biomedical applications and any application with censored observations. This article establishes an orthogonal score for learning treatment effects, using observational data with a potentially large number of confounders. The estimator allows for root-n, asymptotically valid confidence intervals, despite the bias induced by the regularization. Moreover, we develop a novel hazard difference (HDi), estimator. We establish rate double robustness through the cross-fitting formulation. Numerical experiments illustrate the finite sample performance, where we observe that the cross-fitted HDi estimator has the best performance. We study the radical prostatectomy's effect on conservative prostate cancer management through the SEER-Medicare linked data. Last, we provide an extension to machine learning both approaches and heterogeneous treatment effects. for this article are available online.