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      Data Challenges for Externally Controlled Trials: Viewpoint

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          Abstract

          The preferred evidence of a large randomized controlled trial is difficult to adopt in scenarios, such as rare conditions or clinical subgroups with high unmet needs, and evidence from external sources, including real-world data, is being increasingly considered by decision makers. Real-world data originate from many sources, and identifying suitable real-world data that can be used to contextualize a single-arm trial, as an external control arm, has several challenges. In this viewpoint article, we provide an overview of the technical challenges raised by regulatory and health reimbursement agencies when evaluating comparative efficacy, such as identification, outcome, and time selection challenges. By breaking down these challenges, we provide practical solutions for researchers to consider through the approaches of detailed planning, collection, and record linkage to analyze external data for comparative efficacy.

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          Most cited references82

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          The FAIR Guiding Principles for scientific data management and stewardship

          There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
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            Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available.

            Ideally, questions about comparative effectiveness or safety would be answered using an appropriately designed and conducted randomized experiment. When we cannot conduct a randomized experiment, we analyze observational data. Causal inference from large observational databases (big data) can be viewed as an attempt to emulate a randomized experiment-the target experiment or target trial-that would answer the question of interest. When the goal is to guide decisions among several strategies, causal analyses of observational data need to be evaluated with respect to how well they emulate a particular target trial. We outline a framework for comparative effectiveness research using big data that makes the target trial explicit. This framework channels counterfactual theory for comparing the effects of sustained treatment strategies, organizes analytic approaches, provides a structured process for the criticism of observational studies, and helps avoid common methodologic pitfalls.
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              The practical implementation of artificial intelligence technologies in medicine

              The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                2023
                5 April 2023
                : 25
                : e43484
                Affiliations
                [1 ] Cytel, Inc Vancouver, BC Canada
                [2 ] Department of Oncology University of Calgary Calgary, AB Canada
                [3 ] Nashville Biosciences Nashville, TN United States
                Author notes
                Corresponding Author: Louis Dron louis.dron@ 123456cytel.com
                Author information
                https://orcid.org/0000-0001-5958-1223
                https://orcid.org/0000-0003-0783-5759
                https://orcid.org/0000-0002-8577-7278
                https://orcid.org/0000-0001-7131-8874
                https://orcid.org/0000-0002-0768-4228
                https://orcid.org/0000-0002-3276-3948
                Article
                v25i1e43484
                10.2196/43484
                10132012
                37018021
                31033be4-71e6-4642-9030-41f1e6a644ba
                ©Russanthy Ruthiran Velummailum, Chelsea McKibbon, Darren R Brenner, Elizabeth Ann Stringer, Leeland Ekstrom, Louis Dron. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.04.2023.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 12 October 2022
                : 11 January 2023
                : 1 February 2023
                : 19 February 2023
                Categories
                Viewpoint
                Viewpoint

                Medicine
                external control arm,synthetic control arm,single-arm trial,real-world evidence,regulatory approval,data,clinical,decision-making,efficacy,rare conditions,trial

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