Università Degli Stu di di Milano, Milan, Italy; Fondazione I.R.C.C.S. Ca' Granda Ospedale Maggiore Policlinico, Milano, Italy.
Department of Statistics and Quantitative Methods, Laboratory of Healthcare Research & Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy.
Best Pract Res Clin Haematol. 2024 Mar;37(1):101536. doi: 10.1016/j.beha.2024.101536. Epub 2024 Jan 27.
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.
大多数新药的批准都是基于来自大型随机临床试验 (RCT) 的数据。然而，有时来自看似相似的试验会得出相互矛盾的结论，并且这些试验的结论的普遍性是有限的。这些考虑因素部分解释了 RCT 数据的结论与被称为真实世界数据 (RWD) 的注册数据之间的差距。最近，通过人工智能处理的 RWD 的真实世界证据 (RWE) 受到了越来越多的关注。我们描述了在血液学中使用 RWD 的潜力，得出的 RWD 的 RWE 可能会补充 RCT 数据，以支持监管决策。
