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

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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 Forest (RF) to adjust for the high-dimensional covariate to improve efficiency. We study the behavior of the adjusted estimators under mild assumptions and show theoretical guarantees that the proposed estimators are more efficient than the unadjusted ones asymptotically when using RF for the adjustment. In addition, these adjusted estimators are \(\sqrt{n}\)- consistent and asymptotically normally distributed. The finite sample behavior of our methods is studied by simulation. The simulation 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.

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Data Availability

The data that support the findings of this study are available from the center for international blood & marrow transplant research (CIBMTR) but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of CIBMTR.

Code Availability

All code for the simulation and data analysis associated with the current submission is available upon request via email to the corresponding author.

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Funding

R.D. was partially supported by the National Institute of Health via grant 2U54GM115458-06. C.Z. was partially supported by the National Institute of Health via grant 2U54GM115458-06. M.Z. was partly supported by NCATS 12-303G-UN.

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Authors and Affiliations

  1. Department of Biostatistics, University of Nebraska Medical Center, 984375, Nebraska Medical Center, Omaha, NE, 68198, USA

    Ran Dai & Cheng Zheng

  2. Division of Biostatistics, Medical College of Wisconsin, 8701 Watertown Plank Rd, Milwaukee, WI, 53226, USA

    Mei-Jie Zhang

Authors
  1. Ran Dai
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  2. Cheng Zheng
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  3. Mei-Jie Zhang
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Correspondence to Cheng Zheng.

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Dai, R., Zheng, C. & Zhang, MJ. On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes. Stat Biosci 15, 242–260 (2023). https://doi.org/10.1007/s12561-022-09358-2

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  • DOI: https://doi.org/10.1007/s12561-022-09358-2

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