Real World Data in Clinical Research
Real-world data is the bridge between clinical experimentation and the broader, informed use of new medicine in a population. Real-world data enables us to predict how a product may benefit or harm individuals outside of controlled trials. This data helps regulators and life science companies determine if a product is ready for full market authorization (or, possibly, an emergency use authorization), evaluating the safety and efficacy in the clinical experiment. It supports doctors and patients who must decide for themselves if a particular treatment is a right path to take.
Dr. Micheal Kurilla (NCATS) presents data-generating Expended Access programs
As part of the clinical development of therapeutics, vaccines (such as COVID-19 Vaccines), and diagnostics, real-world data is the sum of all information gathered about a product’s impact on individual patients via sources outside of controlled medical research trials. When analyzed and used to make generalizable conclusions, the data becomes real-world evidence.
Real world data comes in three forms:
- Data about the real-world use of a particular product,
- Data about the real-world benefits or side effects of a particular product, and
- Data about how a particular product affects diverse patients in a real-world range of phenotype and genetics in the overall disease population
Real world data and Expanded Access programs:Most important is the knowledge gained by including a wide range of treatment-population in the clinical development process and learning from those patients (Category 3). Traditional clinical research trials try to exclude patients whose disease progression has plateaued, patients with other health conditions (comorbidities), and patients who rely on other medications.
Research trials are also very small relative to the number of patients with unmet medical needs; they’re not able to provide treatment options. But Expanded Access programs have a different goal; they are primarily for treatment. Expanded Access programs have generated vast amounts of real-world data that have supported or quickened the path to market approval. Prominent examples include cancer drugs Gleevec (2000-2003), Iressa (2001-2005), and Lutathera (2017-2019).
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