1. Decentralized Clinical Trials: Scientific Considerations Through the Lens of the Estimand Framework.
- Author
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Izem, Rima, Zuber, Emmanuel, Daizadeh, Nadia, Bretz, Frank, Sverdlov, Oleksandr, Edrich, Pascal, Branson, Janice, Degtyarev, Evgeny, Sfikas, Nikolaos, and Hemmings, Robert
- Subjects
STATISTICAL models ,DECENTRALIZATION in management ,DIFFUSION of innovations ,RESEARCH funding ,CLINICAL trials ,PATIENT-centered care ,COVID-19 pandemic - Abstract
While industry and regulators' interest in decentralized clinical trials (DCTs) is long-standing, the Covid-19 pandemic accelerated and broadened the adoption and experience with these trials. The key idea in decentralization is bringing the clinical trial design, typically on-site, closer to the patient's experience (on-site or off-site). Thus, potential benefits of DCTs include reducing the burden of participation in trials, broadening access to a more diverse population, or using innovative endpoints collected off-site. This paper helps researchers to carefully evaluate the added value and the implications of DCTs beyond the operational aspects of their implementation. The proposed approach is to use the ICH E9(R1) estimand framework to guide the strategic decisions around each decentralization component. Furthermore, the framework can guide the process for clinical trialists to systematically consider the implications of decentralization, in turn, for each attribute of the estimand. We illustrate the use of this approach with a fully DCT case study and show that the proposed systematic process can uncover the scientific opportunities, assumptions, and potential risks associated with a possible use of decentralization components in the design of a trial. This process can also highlight the benefits of specifying estimand attributes in a granular way. Thus, we demonstrate that bringing a decentralization component into the design will not only impact estimators and estimation but can also correspond to addressing more granular questions, thereby uncovering new target estimands. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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