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

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.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (JASA); Vol.118 No.541 March 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 327-342

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CAUSAL INFERENCE
SURVIVAL ANALYSIS
DOUBLE ROBUSTNESS
ORTHOGONAL SCORE

Info Detail Spesifik

https://doi.org/10.1080/01621459.2021.1930546

Pernyataan Tanggungjawab

Jue Hou, Jelena Bradic, Ronghui Xu

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