When medical research is performed, the results are published in a peer-reviewed medical journal – such as the Journal of the American Medical Association (JAMA) or the New England Journal of Medicine (NEJM). Publishing their findings is how scientists participate in the scientific method, which encourages other researchers to try and replicate (or disprove) each other’s conclusions.
Doing Research About Research
What many people don’t know, however, is that it’s possible for other researchers to request the data from these studies in aggregate in order to perform meta-analyses of pharmaceutical data or other information – that is, to perform research about research. Doctors can combine results from different trials in ways that may result in better, more complete, and more reliable data. The scientists who perform these meta-studies are sometimes called “data parasites.”
Big Pharma’s Unkept Promise
Drug studies and clinical trials are no exception to the scientific method. In fact, as far back as 2013, the big pharma industry has proclaimed its “commitment” to pharmaceutical data sharing.
However, evidence is to the contrary. According to a recent article published in the JAMA, only 15.5 percent of research trials have ever had their pharmaceutical data requested by other researchers. Even then, among hundreds of thousands of clinical trials, only 37 metadata analyses have been published.
Imagine A Healthier World
So what would happen if drug companies used shared pharmaceutical data? Some pretty remarkable things, it turns out.
- Data from multiple studies could be used to answer new questions – even those not anticipated by the original researchers – without the expense of launching new trials.
- Better decision-making, as a result of these analyses, could generate $100 billion each year in savings, innovation, and improved efficiency in the healthcare system.
- Risks (or rewards) of newly approved drugs could be identified more quickly, saving patient lives and saving money for pharma companies.
- Big pharma often justifies the high price of their brand-name medicines by invoking the cost of research and development (R&D). Analyses done by pharmaceutical data parasites could reduce both the cost to consumers and the time required to bring the drug to market.
- Predictive modeling methods could be applied across thousands of studies, helping assess potential long-term health risks as well as improved outcomes.
Ultimately, pharmaceutical science is just like any other science: it could learn more from sharing and collaborating than by hoarding information.
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