Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding

Xin Ye, Shu Yang (), Xiaofei Wang and Yanyan Liu
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Xin Ye: Guangdong University of Finance and Economics
Shu Yang: North Carolina State University
Xiaofei Wang: Duke University
Yanyan Liu: Wuhan University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2025, vol. 31, issue 3, No 1, 473-497

Abstract: 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.

Keywords: Causal inference; Hidden confounding; Integrative analysis; Censored data; High dimensional data; 62D20; 62N01; 62N02 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10985-025-09654-1

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