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Titlebook: Real-World Evidence in Medical Product Development; Weili He,Yixin Fang,Hongwei Wang Book 2023 The Editor(s) (if applicable) and The Autho

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發(fā)表于 2025-3-21 18:21:07 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Real-World Evidence in Medical Product Development
編輯Weili He,Yixin Fang,Hongwei Wang
視頻videohttp://file.papertrans.cn/823/822311/822311.mp4
概述A first-of-its-kind, structured end-to-end, comprehensive perspectives from different stakeholders, with real examples.Includes special chapters on estimand, causal inference roadmap, fit-for-purpose
圖書封面Titlebook: Real-World Evidence in Medical Product Development;  Weili He,Yixin Fang,Hongwei Wang Book 2023 The Editor(s) (if applicable) and The Autho
描述.This book provides state-of-art statistical methodologies, practical considerations from regulators and sponsors, logistics, and real use cases for practitioners for the uptake of RWE/D. Randomized clinical trials have been the gold standard for the evaluation of efficacy and safety of medical products. However, the cost, duration, practicality, and limited generalizability have incentivized many to look for alternative ways to optimize drug development. This book provides a comprehensive list of topics together to include all aspects with the uptake of RWE/D, including, but not limited to, applications in regulatory and non-regulatory settings, causal inference methodologies, organization and infrastructure considerations, logistic challenges, and practical use cases..
出版日期Book 2023
關(guān)鍵詞real-world evidence; real-world data; design bias; confounding control; regulatory science; machine learn
版次1
doihttps://doi.org/10.1007/978-3-031-26328-6
isbn_softcover978-3-031-26330-9
isbn_ebook978-3-031-26328-6
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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https://doi.org/10.1007/978-3-031-26328-6real-world evidence; real-world data; design bias; confounding control; regulatory science; machine learn
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發(fā)表于 2025-3-22 06:38:32 | 只看該作者
978-3-031-26330-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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Overview of the Current Real-World Evidence Regulatory Landscaperld. We will first outline the key concepts underpinning evaluation of real-world evidence and discuss similarity and differences in those concepts in guidance documents from different countries. Then, we illustrate the regulatory practice of using real-world data throughout the product development
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Key Considerations in Forming Research Questions and Conducting Research in Real-World Settingred through real-world studies and the analysis of real-world data. The derived real-world evidence may be used to help support approval of a new indication for a medical product that has already been approved or to help support postapproval study requirements. We also discuss how to revise the rese
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Key Variables Ascertainment and Validation in RW Settingam. ., 2018). Key variables are those that define a key patient characteristic necessary to identify the study population of interest, the treatment/exposure, the outcome, or a key confounder whose effect must be adjusted for. With Real-World Data (RWD), often (1) exposure, outcomes, and key confoun
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