Augmenting Both Arms of a Randomized Controlled Trial Using External Data: An Application of the Propensity Score-Integrated Approaches

Heng Li, Wei-Chen Chen, Chenguang Wang, Nelson Lu, Changhong Song, Ram Tiwari, Yunling Xu and Lilly Q. Yue ()
Additional contact information
Heng Li: U.S. Food and Drug Administration
Wei-Chen Chen: U.S. Food and Drug Administration
Chenguang Wang: Johns Hopkins University
Nelson Lu: U.S. Food and Drug Administration
Changhong Song: U.S. Food and Drug Administration
Ram Tiwari: U.S. Food and Drug Administration
Yunling Xu: U.S. Food and Drug Administration
Lilly Q. Yue: U.S. Food and Drug Administration

Statistics in Biosciences, 2022, vol. 14, issue 1, No 5, 79-89

Abstract: Abstract 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.

Keywords: Power prior; Composite likelihood; Outcome-free design (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12561-021-09315-5

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