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真实世界证据与产品开发:机遇、挑战与风险缓解。

Real-world evidence and product development: Opportunities, challenges and risk mitigation.

机构信息

Department of Nephrology, Inkosi Albert Luthuli Central Hospital, Durban, South Africa.

Clinical Operations and Medical Affairs, Sanofi, Midrand, South Africa.

出版信息

Wien Klin Wochenschr. 2021 Aug;133(15-16):840-846. doi: 10.1007/s00508-021-01851-w. Epub 2021 Apr 9.

DOI:10.1007/s00508-021-01851-w
PMID:33837463
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8034870/
Abstract

Real-world evidence (RWE) is derived from real-world data (RWD) sources including electronic health records, claims data, registries (disease, product) and pragmatic clinical trials. The importance of RWE derived from RWD has been once again demonstrated during the coronavirus disease 2019 (COVID-19) pandemic, as it can improve patient care by complementing information obtained from traditional clinical trial programs. Additionally, RWE can generate insights into disease mechanisms, epidemiology, patient flows in and out of healthcare systems, and drivers and barriers to optimal clinical care in real-world settings. Identifying unmet medical needs is crucial as it often can inform which investigational new drugs enter clinical trial testing, and RWE studies from hospital settings have contributed substantial progress here. RWE can also optimize the design of clinical studies, inform benefit risk assessments and use networks of pragmatic studies to help with clinical trial feasibilities and eventual trial initiation. The challenges of RWD include data quality, reproducibility and accuracy which may affect validity. RWD and RWE must be fit for purpose and one must be cognizant of inherent biases.

摘要

真实世界证据(RWE)来源于真实世界数据(RWD)来源,包括电子健康记录、理赔数据、登记处(疾病、产品)和实用临床试验。在 2019 年冠状病毒病(COVID-19)大流行期间,再次证明了源自 RWD 的 RWE 的重要性,因为它可以通过补充传统临床试验项目获得的信息来改善患者护理。此外,RWE 可以深入了解疾病机制、流行病学、患者在医疗保健系统中的进出流动情况,以及在真实环境中实现最佳临床护理的驱动因素和障碍。确定未满足的医疗需求至关重要,因为它通常可以告知哪些新药进入临床试验测试,而来自医院环境的 RWE 研究在这里做出了重大贡献。RWE 还可以优化临床试验的设计,为获益风险评估提供信息,并利用实用研究网络来帮助临床试验的可行性和最终启动。RWD 面临的挑战包括数据质量、可重复性和准确性,这些因素可能会影响有效性。RWD 和 RWE 必须符合目的,并且必须意识到固有的偏见。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2096/8034870/3c7f61c35a1d/508_2021_1851_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2096/8034870/3c7f61c35a1d/508_2021_1851_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2096/8034870/3c7f61c35a1d/508_2021_1851_Fig1_HTML.jpg

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