Goldacre on Pharma Research Credibility

Ben Goldacre is once again arguing that pharmaceutical “industry-funded trials are too common, can’t be trusted — and bring pills to market that likely don’t work.” The NY Times features his argument today. He has exhaustively compiled problematic practices that add up to a shocking claim: “the entire evidence base for medicine has been undermined by a casual lack of transparency.” For example, here’s one vignette from his most recent book:

In October 2010, a group of researchers were finally able to bring together all the trials that had ever been conducted on reboxetine. Through a long process of investigation — searching in academic journals but also arduously requesting data from the manufacturers and gathering documents from regulators — they were able to assemble all the data, both from trials that were published and from those that had never appeared in academic papers.

When all this trial data was put together it produced a shocking picture. Seven trials had been conducted comparing reboxetine against placebo. Only one, conducted in 254 patients, had a neat, positive result, and that one was published in an academic journal for doctors and researchers to read. But six more trials were conducted in almost 10 times as many patients. All of them showed that reboxetine was no better than a dummy sugar pill. None of these trials were published. I had no idea they existed.

I have not come across a convincing industry, FDA, or Richard Epstein response to Goldacre’s work. (I don’t find this brief letter particularly compelling.). He offers several case studies like the reboxetine one. Isn’t it time to fund systematic reviews of the evidence of effectiveness on all drugs?

A recent lawsuit against the FDA will test its usual trade secret rationale for failing to require drug companies to release all data from trials. If it succeeds, systematic reviews may be easier to complete, and will help save patients from the scourge of unnecessary or ineffective drugs. In the current information environment, perhaps the best hope we have is big data leading to more personalized comparisons of treatment effectiveness.

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