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

Jue Hou, Jelena Bradic and Ronghui Xu

Journal of the American Statistical Association, 2023, vol. 118, issue 541, 327-342

Abstract: 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. Supplementary materials for this article are available online.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01621459.2021.1930546 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlasa:v:118:y:2023:i:541:p:327-342

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UASA20

DOI: 10.1080/01621459.2021.1930546

Access Statistics for this article

Journal of the American Statistical Association is currently edited by Xuming He, Jun Liu, Joseph Ibrahim and Alyson Wilson

More articles in Journal of the American Statistical Association from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().