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Abstract In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimensional. We utilize data from both the randomized controlled trial (RCT), considered as the gold standard, and real-world data (RWD), possibly affected by hidden confounding factors. To achieve a more efficient HTE estimate, such integrative analysis requires great insight into the data generation mechanism, particularly the accurate characterization of unmeasured confounding effects/bias. With this aim, we propose a penalized-regression-based integrative approach that allows for the simultaneous estimation of parameters, selection of variables, and identification of the existence of unmeasured confounding effects. The consistency, asymptotic normality, and efficiency gains are rigorously established for the proposed estimate. Finally, we apply the proposed method to estimate the HTE of lobar/sublobar resection on the survival of lung cancer patients. The RCT is a multicenter non-inferiority randomized phase 3 trial, and the RWD comes from a clinical oncology cancer registry in the United States. The analysis reveals that the unmeasured confounding exists and the integrative approach does enhance the efficiency for the HTE estimation.
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This content will become publicly available on June 1, 2026
Post‐selection inference for high‐dimensional mediation analysis with survival outcomes
ABSTRACT It is of substantial scientific interest to detect mediators that lie in the causal pathway from an exposure to a survival outcome. However, with high‐dimensional mediators, as often encountered in modern genomic data settings, there is a lack of powerful methods that can provide valid post‐selection inference for the identified marginal mediation effect. To resolve this challenge, we develop a post‐selection inference procedure for the maximally selected natural indirect effect using a semiparametric efficient influence function approach. To this end, we establish the asymptotic normality of a stabilized one‐step estimator that takes the selection of the mediator into account. Simulation studies show that our proposed method has good empirical performance. We further apply our proposed approach to a lung cancer dataset and find multiple DNA methylation CpG sites that might mediate the effect of cigarette smoking on lung cancer survival.
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- Award ID(s):
- 2112938
- PAR ID:
- 10627800
- Publisher / Repository:
- John Wiley & Sons
- Date Published:
- Journal Name:
- Scandinavian Journal of Statistics
- Volume:
- 52
- Issue:
- 2
- ISSN:
- 0303-6898
- Page Range / eLocation ID:
- 756 to 776
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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