利用外部数据增强随机对照试验的两个分支:倾向得分综合方法的应用。

Pub Date : 2022-01-01 Epub Date: 2021-06-19 DOI:10.1007/s12561-021-09315-5
Heng Li, Wei-Chen Chen, Chenguang Wang, Nelson Lu, Changhong Song, Ram Tiwari, Yunling Xu, Lilly Q Yue
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引用次数: 6

摘要

利用外部数据是最近备受关注的一个主题。倾向得分综合方法是为此目的在方法论上的创新。在本文中,我们采用了这些方法,最初是为了用外部数据增加单组研究,用于用外部数据增加随机对照试验(RCT)的两组。在概述了基本思想之后,我们提供了一个循序渐进的教程,介绍如何在RCT环境中实施倾向得分综合方法,从研究设计到结果分析,以保持研究的完整性和客观性。包括贝叶斯(幂先验)方法和频率(复合似然)方法。本文最后还概述了这些方法的一些扩展和变体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches.

Leveraging external data is a topic that have recently received much attention. The propensity score-integrated approaches are a methodological innovation for this purpose. In this paper we adapt these approaches, originally introduced to augment single-arm studies with external data, for the augmentation of both arms of a randomized controlled trial (RCT) with external data. After recapitulating the basic ideas, we provide a step-by-step tutorial of how to implement the propensity score-integrated approaches, from study design to outcome analysis, in the RCT setting in such a way that the study integrity and objectively are maintained. Both the Bayesian (power prior) approach and the frequentist (composite likelihood) approach are included. Some extensions and variations of these approaches are also outlined at the end of this paper.

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