Using real-world evidence in haematology

IF 2.2 4区医学 Q3 HEMATOLOGY

Best Practice & Research Clinical Haematology Pub Date : 2024-01-27 DOI:10.1016/j.beha.2024.101536

Francesco Passamonti , Giovanni Corrao , Gastone Castellani , Barbara Mora , Giulia Maggioni , Matteo Giovanni Della Porta , Robert Peter Gale

引用次数: 0

Abstract

Most new drug approvals are based on data from large randomized clinical trials (RCTs). However, there are sometimes contradictory conclusions from seemingly similar trials and generalizability of conclusions from these trials is limited. These considerations explain, in part, the gap between conclusions from data of RCTs and those from registries termed real world data (RWD). Recently, real-world evidence (RWE) from RWD processed by artificial intelligence has received increasing attention. We describe the potential of using RWD in haematology concluding RWE from RWD may complement data from RCTs to support regulatory decisions.

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在血液学中使用真实世界的证据

大多数新药的批准都是基于大型随机临床试验（RCT）的数据。然而，从看似相似的试验中得出的结论有时会相互矛盾，而且这些试验结论的推广性也很有限。这些因素在一定程度上解释了从随机临床试验数据中得出的结论与从被称为真实世界数据（RWD）的登记数据中得出的结论之间的差距。最近，由人工智能处理的真实世界数据（RWD）得出的真实世界证据（RWE）受到越来越多的关注。我们介绍了在血液学中使用真实世界数据的潜力，并得出结论：来自真实世界数据的 RWE 可以补充 RCT 数据，为监管决策提供支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Best Practice & Research Clinical Haematology 医学-血液学

CiteScore

4.20

自引率

0.00%

发文量

审稿时长

35 days

期刊介绍： Best Practice & Research Clinical Haematology publishes review articles integrating the results from the latest original research articles into practical, evidence-based review articles. These articles seek to address the key clinical issues of diagnosis, treatment and patient management. Each issue follows a problem-orientated approach which focuses on the key questions to be addressed, clearly defining what is known and not known, covering the spectrum of clinical and laboratory haematological practice and research. Although most reviews are invited, the Editor welcomes suggestions from potential authors.