On High Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes
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We study the estimation of 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 adjustment with high dimensional covariates to improve efficiency. We study the behavior of general adjusted estimator when the adjustments are `risk consistent' and `jackknife compatible'. The theoretical results provided guarantee that the estimators we proposed are more efficient than the unadjusted one asymptotically when using SRF for adjustment. The finite sample behavior of our methods are studied by simulation, and the results are in agreement with our theoretical results. We also illustrated our methods via 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.
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