John Ioannidis Is Just What Science Needs

For those of you who haven’t seen my video on “Why Most Research Findings Are False” or otherwise aren’t familiar with the excellent work of John PA Ioannidis, I invite you to learn more about his work:

He’s done such an important job in being the conscience of clinical science: exposing where the scientists are prone to self-deception.  He’s a critical thinker about critical thinking, and I love him for it.  Apparently, so do other scientists, because his article is the most downloaded from the Public Library of Science (PLoS) site.  His paper was also the clearest on “prior probability” and Bayesian statistical methods that I have ever read.

So now I come across his work in my background research on antidepressants, and as usual it’s a clear and revealing look at the critical thinking that should underlie the body of research.  Here’s the full-text article:

Effectiveness of antidepressants: an evidence myth constructed from a thousand randomized trials?

His wording is forceful and unapologetic:

“Based on the above considerations, antidepressants are probably indicated only in select patients with major depression, probably preferentially in those who have severe symptoms and have not responded to anything else. For most patients with some depressive symptoms who are currently taking antidepressants, using these drugs would not have been the preferred option, placebo would be practically as good, if not better, and would save toxicities and cost.”

Did you catch that?  Placebo would have been as effective, or more effective, in many cases without the side effects.  I have to concur based on the systematic reviews I’ve screened so far.  I think the number needed to treat says a lot as well, and will probably include that concept in the future video on the topic.  It’s not that they’re ineffective, it’s that they’re badly misused, overdiagnosed, and I think a big part of the blame can fall on pharma advertising on the airwaves.  The numbers below say a lot about why we keep generating new antidepressants:

Table 1
Top-selling antidepressants in the USA, 2006
Drug (brand name) Rank across all drugs Sales (billions $)
Venlafaxine XR (Effexor XR) 6 2.25
Escitalopram (Lexapro) 10 2.10
Sertraline (Zoloft) 15 1.77
Bupropion XL (Wellbutrin XL) 16 1.67
Duloxetine (Cymbalta) 35 1.08

5 comments on “John Ioannidis Is Just What Science Needs

  1. Firstly, the disclaimers: I have not done any research or statistical tests for the following. This is just a hunch (even if it could be called an informed one, it’s still just a hunch), so don’t bet any money on this… 😉
    I have a suspicion that modern antidepressants’ (mainly SSRI) assumed quasi-complete ineffectiveness is as false as their hyper-effectiveness promoted by the industry. The reason for this might be that this class of drugs, naively thought by many practitioners to be “very low risk”, has been prescribed to almost every patient and their dog. Furthermore, psychiatric therapy still hasn’t completely cleaned its act from past “sins” and many MDs still consider the drugs as the uber-weapon to control psychiatric disorders (whilst, in most cases, it should be thought as the catalyst for an effective psychotherapy of some sort or the other).
    We should all bare in mind that CNS disorders, especially – but not only – psychiatric ones, are far more complicated than too much of this or too few of that neurotransmitter but rather some disregulation of patterns in neuron response. Therefore, flushing a bucketful of an agonist or antagonist or effector on metabolism across the brain is more like trying to fix a swiss watch with a hammer. It’s only logical then to expect the most significant net effects in the most severe of cases while mild ones show no better effects than placebo.

  2. C0nc0rdance. If it is true, and most scientific research findings are false, would that mean that: most of your videos based on published research findings reach the wrong conclusions?

    • That’s why I rely on reviews and multiple papers as well as trying to account for prior probability. Not all papers are equally false, as you might imagine. “Out of the blue” case-control trials are often wrong, but confirmatory research that’s built on loads of mechanistic/basic research is very often right. Fields where we understand the underlying mechanism of action have a higher hit to miss ratio.

  3. I have read much of Ioannidis’ work, and as a researcher familiar with the use of statistics in science, I have been very excited by his work. Ioannidis has published on the same kind of problems in many other areas of medicine as well.

    Thank you for your posts on these critical issues.

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