On High Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes

TL;DR

This paper introduces new high-dimensional covariate adjustment methods using regularized survival regression and survival random forests to improve the efficiency of causal effect estimation in survival trials with right-censoring.

Contribution

It proposes novel estimators that leverage regularized survival models and random forests, providing theoretical guarantees of efficiency and asymptotic normality in high-dimensional settings.

Findings

Adjusted estimators outperform unadjusted ones asymptotically.

Estimators are $ oot 2$-consistent and asymptotically normal.

Simulation results confirm theoretical advantages.

Abstract

The purpose of this work is to improve the efficiency in estimating the average causal effect (ACE) on the survival scale where right-censoring exists and high-dimensional covariate information is available. We propose new estimators using regularized survival regression and survival random forests (SRF) to make the adjustment for the high dimensional covariates to improve efficiency. We study the behavior of the adjusted estimator under mild assumptions and show theoretical guarantees that the proposed estimators are more efficient than the unadjusted ones asymptotically when using SRF for adjustment. In addition, these adjusted estimators are $\sqrt{n}$- consistent and asymptotically normally distributed. The finite sample behavior of our methods are studied by simulation, and the results are in agreement with the theoretical results. We also illustrate our methods by analyzing the real data from transplant research to identify the relative effectiveness of identical sibling donors compared to unrelated donors with the adjustment of cytogenetic abnormalities.