Thursday, April 25, 2019

The EOLIA ECMO Bayesian Reanalysis in JAMA

A Hantavirus patient on ECMO, circa 2000
Spoiler alert:  I'm a Bayesian decision maker (although maybe not a Bayesian trialist) and I "believe" in ECMO as documented here.

My letter to the editor of JAMA was published today (and yeah I know, I write too many letters, but hey, I read a lot and regular peer review often doesn't cut it) and even when you come at them like a spider monkey, the authors of the original article still get the last word (and they deserve it - they have done far more work than the post-publication peer review hecklers with their quibbles and their niggling letters.)

But to set some thing clear, I will need some more words to elucidate some points about the study's interpretation.  The authors' response to my letter has five points.
  1. I (not they) committed confirmation bias, because I postulated harm from ECMO.  First, I do not have a personal prior for harm from ECMO, I actually think it is probably beneficial in properly selected patients, as is well documented in the blog post from 2011 describing my history of experience with it in hantavirus, and as well in a book chapter I wrote in Cardiopulmonary Bypass Principles and Practice circa 2006.  There is irony here - I "believe in" ECMO, I just don't think their Bayesian reanalysis supports my (or anybody's) beliefs in a rational way!  The point is that it was a post hoc unregistered Bayesian analysis after a pre-registered frequentist study which was "negative" (for all that's worth and not worth), and the authors clearly believe in the efficacy of ECMO as do I.  In finding shortcomings in their analysis, I seek to disconfirm or at least challenge no only their but my own beliefs.  And I think that if the EOLIA trial had been positive, that we would not be publishing Bayesian reanalyses showing how the frequentist trial may be a type I error.  We know from long experience that if EOLIA had been "positive" that success would have been declared for ECMO as it has been with prone positioning for ARDS.  (I prone patients too.)  The trend is to confirm rather than to disconfirm, but good science relies more on the latter.
  2. That a RR of 1.0 for ECMO is a "strongly skeptical" prior.  It may seem strong from a true believer standpoint, but not from a true nonbeliever standpoint.  Those are the true skeptics (I know some, but I'll not mention names - I'm not one of them) who think that ECMO is really harmful on the net, like intensive insulin therapy (IIT) probably is.  Regardless of all the preceding trials, if you ask the NICE-SUGAR investigators, they are likely to maintain that IIT is harmful.  Importantly, the authors skirt the issue of the emphasis they place on the only longstanding and widely regarded as positive ARDS trial (of low tidal volume).  There are three decades of trials in ARDS patients, scores of them, enrolling tens of thousands of patients, that show no effect of the various therapies.  Why would we give primacy to the the one trial which was positive, and equate ECMO to low tidal volume?  Why not equate it to high PEEP, or corticosteroids for ARDS?  A truly skeptical prior would have been centered on an aggregate point estimate and associated distribution of 30 years of all trials in ARDS of all therapies (the vast majority of them "negative").  The sheer magnitude of their numbers would narrow the width of the prior distribution with RR centered on 1.0 (the "severely skeptical" one), and it would pull the posterior more towards zero benefit, a null result.  Indeed, such a narrow prior distribution may have shown that low tidal volume is an outlier and likely to be a false positive (I won't go any farther down that perilous path).  The point is, even if you think a RR of 1.0 is severely skeptical, the width of the distribution counts for a lot too, and the uninitiated are likely to miss that important point.
  3. Priors are not used to "boost" the effect of ECMO.  (My original letter called it a Bayesian boost, borrowing from Mayo, but the adjective was edited out.) Maybe not always, but that was the effect in this case, and the respondents did not cite any examples of a positive frequentist result that was reanalyzed with Bayesian methods to "dampen" the observed effect.  It seems to only go one way, and that's why I alluded to confirmation bias.  The "data-driven priors" they published were tilted towards a positive result, as described above.
  4. Evidence and beliefs.  But as Russell said "The degree to which beliefs are based on evidence is very much less than believers suppose."  I support Russell's quip with the aforementioned.
  5. Judgment is subjective, etc.  I would welcome a poll, in the spirit of crowdsourcing, as we did here to better understand what the community thinks about ECMO (my guess is it's split ratherly evenly, with a trend, perhaps strong, for the efficacy of ECMO).  The authors' analysis is laudable, but it is not based on information not already available to the crowd; rather it transforms it in ways may not be transparent to the crowd and may magnify it in a biased fashion if people unfamiliar with Bayesian methods do not scrutinize the chosen prior distributions.


  1. Here, on the NEJM website is a poll, showing 81% would initiate ECMO for the patient in the vignette, suggesting much stronger support than I anticipated for ECMO. There were 4000 responses. It is a convenience sample with unknown representativeness, but the effect is strong, so I'm guessing the majority of the community at large does indeed support ECMO, at least for persistent arterial hypoxemia with sats in the 80-82% range, like was presented in the vignette.



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