Kokkotou, E.; Anagnostakis, M.; Evangelou, G.; Syrigos, N.K.; Gkiozos, I. Real-World Data and Evidence in Lung Cancer: A Review of Recent Developments. Cancers2024, 16, 1414.
Kokkotou, E.; Anagnostakis, M.; Evangelou, G.; Syrigos, N.K.; Gkiozos, I. Real-World Data and Evidence in Lung Cancer: A Review of Recent Developments. Cancers 2024, 16, 1414.
Kokkotou, E.; Anagnostakis, M.; Evangelou, G.; Syrigos, N.K.; Gkiozos, I. Real-World Data and Evidence in Lung Cancer: A Review of Recent Developments. Cancers2024, 16, 1414.
Kokkotou, E.; Anagnostakis, M.; Evangelou, G.; Syrigos, N.K.; Gkiozos, I. Real-World Data and Evidence in Lung Cancer: A Review of Recent Developments. Cancers 2024, 16, 1414.
Abstract
Conventional cancer clinical trials can be time-consuming and expensive, often yielding results with limited applicability to real-world scenarios and presenting challenges for patient participation. Real-world data (RWD) studies offer a promising solution to address evidence gaps and provide essential information about the effects of cancer treatments in real-world settings. Defining RWD based on its original intent, which is the collection of data at the point of care for clinical purposes, can distinguish it from conventional clinical trial data. RWD can be generated using experimental designs that resemble those used in conventional clinical trials, but with several advantages, such as improved efficiency and a better balance between internal and external validity. RWD can support pharmacovigilance, insights into disease progression, and the development of external control groups. Prospective RWD collection enables evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. To ensure the quality of real-world studies, it is crucial to implement auditable data abstraction methods and develop new incentives to capture clinically relevant data electronically at the point of care. The treatment landscape is constantly evolving, with the integration of front-line immune checkpoint inhibitors (ICIs), either alone or in combination with chemotherapy, affecting subsequent treatment lines. Real-world effectiveness and safety in underrepresented populations, such as the elderly, patients with poor performance status (PS), hepatitis, or human immunodeficiency virus-infected patients, are still largely unexplored. Similarly, the cost-effectiveness and sustainability of these innovative agents are important considerations in the real world.
Keywords
oncology; real-world data; real-world evidence; epidemiology; safety; efficacy; artificial intelligence; machine learning; data quality; lung cancer
Subject
Medicine and Pharmacology, Oncology and Oncogenics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
,
,
