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多区域随机临床试验治疗效果异质性评估

Assessment of treatment effect heterogeneity for multiregional randomized clinical trials.

作者信息

Zhuang Haotian, Wang Xiaofei, George Stephen L

机构信息

Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

出版信息

Stat Biopharm Res. 2025;17(3):315-322. doi: 10.1080/19466315.2024.2421748. Epub 2024 Dec 20.

Abstract

Multiregional clinical trials (MRCTs) have become increasingly common in recent years. Detecting underlying regional heterogeneity is a critical issue for these trials. Existing methods for assessing treatment effect heterogeneity across regions have ignored the incomparability of baseline extrinsic risk factors of the randomized patients from different regions. In this paper, a calibration weighting method is proposed to calibrate the distribution of these extrinsic risk factors between multiple regions. We establish the consistency and the asymptotic normality of the calibration weighting estimator. Simulation studies confirm the finite sample properties of the proposed estimator as well as its superior performance over naive methods and the inverse probability weighting method. The proposed method is illustrated using a randomized clinical trial of adjuvant chemotherapy for resected non-small-cell lung cancer.

摘要

近年来,多区域临床试验(MRCTs)越来越普遍。检测潜在的区域异质性是这些试验的关键问题。现有的评估不同区域治疗效果异质性的方法忽略了来自不同区域的随机分组患者基线外部风险因素的不可比性。在本文中,我们提出了一种校准加权方法来校准多个区域之间这些外部风险因素的分布。我们建立了校准加权估计量的一致性和渐近正态性。模拟研究证实了所提出估计量的有限样本性质以及它相对于简单方法和逆概率加权方法的优越性能。通过一项针对切除的非小细胞肺癌辅助化疗的随机临床试验对所提出的方法进行了说明。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a9cc/12312651/c04071b05443/nihms-2033841-f0001.jpg

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本文引用的文献

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Improving trial generalizability using observational studies.利用观察性研究提高试验的概括性。
Biometrics. 2023 Jun;79(2):1213-1225. doi: 10.1111/biom.13609. Epub 2022 Jan 11.

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