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关于 Chen 等人(2022 年)的述评:为使 RCT 更有效率,需要继续研究如何利用次要结局指标中的信息的方法。

Commentary on Chen et al. (2022): The need for continued methodological research on leveraging information in secondary endpoints for more efficient RCTs.

机构信息

Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, 2221 University Ave SE, Minneapolis, MN 55414, USA.

Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, 2221 University Ave SE, Minneapolis, MN 55414, USA.

出版信息

Contemp Clin Trials. 2024 Oct;145:107664. doi: 10.1016/j.cct.2024.107664. Epub 2024 Aug 18.

Abstract

Chen et al. (2022) recently proposed a set of estimating equations that incorporate data from secondary endpoints to improve precision in parameter estimates related to a primary endpoint. We were motivated to translate their methodology to the context of randomized controlled trials to gain precision in treatment effect estimation using data from secondary endpoints. Our results suggest that this estimator cannot gain efficiency in this context because of random treatment assignment, especially when there is a treatment effect on secondary endpoints, and that further methodological work in this area is needed.

摘要

陈等人(2022 年)最近提出了一组估算方程,该方程纳入了次要终点的数据,以提高与主要终点相关的参数估计的精度。我们受到启发,将他们的方法学转化为随机对照试验的背景,以便从次要终点的数据中获得治疗效果估计的精度。我们的结果表明,由于随机治疗分配,这个估计器在这种情况下不能提高效率,特别是当次要终点存在治疗效果时,因此需要在这一领域进一步开展方法学工作。

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