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.

Using real-world evidence in haematology / F. Passamonti, G. Corrao, G. Castellani, B. Mora, G. Maggioni, M.G. Della Porta, R.P. Gale. - In: BAILLIERE'S BEST PRACTICE IN CLINICAL HAEMATOLOGY. - ISSN 1521-6926. - 37:1(2024 Mar), pp. 101536.1-101536.5. [10.1016/j.beha.2024.101536]

Using real-world evidence in haematology

F. Passamonti
Primo
;
2024

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.
Artificial intelligence; Haematological cancers; Leukemia; Lymphoma; Real world data; Real world evidence
Settore MEDS-09/B - Malattie del sangue
mar-2024
Article (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1137758
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