In the United States, the FDA requires that every clinical trial or application to market a new drug or biologic be accompanied by clinical study data showing the safety and efficacy of the proposed product. Converting clinical data into the FDA-required CDISC standards is a difficult, time-consuming and error-prone process; if not done correctly, the FDA might refuse to file the application, or send it back to the sponsoring organization for correction. These delays in regulatory approval are very costly for pharmaceutical companies. Every day in delay means that the drug is not on the market, and therefore not generating any revenue during the limited time that the patent holds. Tamr significantly reduces the time and effort required for CDISC conversion by simultaneously using two powerful methods: advanced machine learning and expert crowdsourcing. This paper describes in detail how Tamr for CDISC works, and its implication for pharmaceutical companies.
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