Article ; Online: Contributions of Real-World Evidence and Real-World Data to Decision-Making in the Management of Soft Tissue Sarcomas.
2021 Volume 99 Suppl 1, Page(s) 3–7
Abstract: ... claims through to electronic surveillance data (e.g., activity trackers). Real-world data derive ...
Abstract | Real-world data are defined as data relating to any aspect of a patient's health status collected in the context of routine health surveillance and medical care delivery. Sources range from insurance billing claims through to electronic surveillance data (e.g., activity trackers). Real-world data derive from large populations in diverse clinical settings and thus can be extrapolated more readily than clinical trial data to patients in different clinical settings or with a variety of comorbidities. Real-world data are used to generate real-world evidence, which might be regarded as a "meta-analysis" of accumulated real-world data. Increasingly, regulatory authorities are recognizing the value of real-world data and real-world evidence, especially for rare diseases where it may be practically unfeasible to conduct randomized controlled trials. However, the quality of real-world evidence depends on the quality of the data collected which, in turn, depends on a correct pathological diagnosis and the homogeneous behaviour of a reliably defined and consistent disease entity. As each of the more than 80 varieties of soft tissue sarcoma (STS) types represents a distinct disease entity, the situation is exceedingly complicated. Discordant diagnoses, which affect data quality, present a major challenge for use of real-world data. As real-world data are difficult to collect, collaboration across sarcoma reference institutions and sophisticated information technology solutions are required before the potential of real-world evidence to inform decision-making in the management of STS can be fully exploited. |
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MeSH term(s) | Clinical Trials as Topic ; Data Collection ; Decision Making ; Humans ; Information Technology ; Sarcoma/therapy |
Language | English |
Publishing date | 2021-04-14 |
Publishing country | Switzerland |
Document type | Journal Article ; Review |
ZDB-ID | 250101-6 |
ISSN | 1423-0232 ; 0030-2414 |
ISSN (online) | 1423-0232 |
ISSN | 0030-2414 |
DOI | 10.1159/000515266 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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