With the ongoing focus on healthcare outcomes-based payment models, pharmaceutical companies face powerful pressures to demonstrate not just safety and efficacy of a new treatment, but also both cost effectiveness and comparative effectiveness. This means they must show that their agent is not only better than placebo but also better than other agents. Comparative effectiveness of any particular treatment can be established by interventional clinical trials, observational real-world evidence studies, or systematic review and meta-analysis. Access to on-going and past clinical trials via trial registries provides much valuable information, but effective search can be hindered by issues such as search vocabularies and problems of searching the unstructured text.
Merck recently published a paper, demonstrating the success of a text-mining pipeline that overcomes these issues and extracts key information for comparative effectiveness research from clinical trial registries. Researchers in the Informatics IT group wanted to search clinical trial registries (NIH ClinicalTrials.gov, WHO International Clinical Trials Registry Platform (ICTRP), and Citeline Trialtrove) and synthesize comparative effectiveness data for a set of Merck drugs, in order to: