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Yearly Archives: 2015

June 26, 2015 no comments

Viagra, Melanoma And The Problem With Big Data

This week saw the climax (or, for those who like to demonize the global pharmaceutical industry, perhaps the anti-climax) of a year-long saga linking an increased risk of melanoma with drugs such as sildenafil (better known as Viagra) used to treat erectile dysfunction.


The story began in 2014 when a team of Boston scientists observed higher rates of melanoma among men taking Viagra. Both the original paper in JAMA Internal Medicine, and the accompanying editorial, encouraged caution: the observational nature of the study design made it impossible to know whether the drugs increased the risk of melanoma (a causal effect) or were merely associated with it (a correlation).


But expressing an abundance of caution about the interpretation of the data in the primary scientific literature unsurprisingly did not prevent a great deal of concern among users of the drugs in question. While doctors couldn’t conclude the link was causal, equally they could not reassure patients that the association was benign either. Some men undoubtedly stopped taking the drug just in case the risks were real.


This is not the first time patients have been left with such decisions, and it most certainly will not be the last. Vast datasets collected by US insurance companies have already thrown up a number of such “candidate associations” between individual drugs and particular side-effects (such as this study mining hundreds of thousands of insurance claim records to reveal an association between use of SSRIs, such as Prozac, and bone fractures). And the developed world is putting in place ever more sophisticated data collection processes embedded in the healthcare system (such as centralized electronic medical records).


Because of the huge numbers of patients in these datasets, it is possible to see even tiny associations with statistical confidence (associations that would have disappeared into the noise in even the largest pre-approval clinical trials). Many of these datasets already dwarf the 20,000 or so patient records that led to the original observation linking Viagra and melanoma. With a million patients, even a 1% increase in risk could be reliably identified.


What happened next was a case-study in epidemiological sleuthing: Stacy Loeb and her colleagues at New York University looked carefully at another dataset, this time from Sweden. Interestingly, the same association was observed a second time (effectively eliminating the possibility that the original observation was a statistical fluke). But looking more closely, they were able to conclude that the association was more than likely correlation and not causation.


Most tellingly, there was no link …

June 4, 2015 no comments

A DrugBaron Glossary

To mark the fifth anniversary of this blog, …


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