Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician

By L. Blonde, Kamlesh Khunti, S. Harris, C. Meizinger, N. Skolnik. Advances in Therapy, 2018

648 citations recorded by the source corpus.

Real-world studies have become increasingly important in providing evidence of treatment effectiveness in clinical practice. While randomized clinical trials (RCTs) are the “gold standard” for evaluating the safety and efficacy of new therapeutic agents, necessarily strict inclusion and exclusion criteria mean that trial populations are often not representative of the patient populations encountered in clinical practice. Real-world studies may use information from electronic health and claims databases, which provide large datasets from diverse patient populations, and/or may be observational, collecting prospective or retrospective data over a long period of time. They can therefore provide information on the long-term safety, particularly pertaining to rare events, and effectiveness of drugs in large heterogeneous populations, as well as information on utilization patterns and health and economic outcomes. This review focuses on how evidence from real-world studies can be utilized to complement data from RCTs to gain a more complete picture of the advantages and disadvantages of medications as they are used in practice.Funding: Sanofi US, Inc.

Subfields: Endocrinology, Diabetes and Metabolism, Epidemiology, Geriatrics and Gerontology

View Paper: https://www.semanticscholar.org/paper/0069bc3d73ac2d587fdb5fd1e16214307f3f1d88

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Citations in the corpus

• Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods (2019, methodology): “However, caution is advised when using data that are “too complete” or “too controlled,” since it may not represent the real-world population and contribute to overfitting, discussed later in this article.(9) Ultimately, the best and most balanced approach is to pilot AI/ML algorithms using more con”