Fuller, Jonathan

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Contents lists available at ScienceDirectStudies in History and Philosophy of Biol & Biomed Scijournal homepage: www.elsevier.com/locate/shpscBook ForumMedical Nihilism by Jacob Stegenga: What is theright dose?“We should have little confidence in the effectiveness of medicalinterventions” (2018, p. 167). This rattling statement is the thesis thatJacob Stegenga calls medical nihilism in his book of the same name.Stegenga's monograph is a philosophical analysis and criticism of con-temporary medical research that dissects the concepts, researchmethods and social context of modern medicine. In building his case,Stegenga exposes vulnerabilities in our concepts and methods that haveserved as sites for various biases and interests to corrupt the evidencebase for medical therapies, especially drugs.Stegenga's book is analytic, persuasive, eminently readable andengages enthusiastically with the medical literature. It is a toweringcontribution to philosophy of science and the burgeoning field of phi-losophy of medicine. I trained in medicine as well as in philosophy. Thisbook speaks forcefully to both parts of me. Serious criticism of medicinehas often been left to other humanities disciplines. Important socio-logical, anthropological and historical critiques of medicine have beenwritten, but no philosopher of science has wielded the tools of analyticphilosophy to launch a book-length critique quite like Stegenga does inMedical Nihilism, which now belongs to a body of critical literature thatincludes classics like Illich’s (1975) Medical Nemesis, Foucault's (1965)Madness and Civilization, Wootton's (2007) Bad Medicine, andMcKeown's (1980) The Role of Medicine: Dream, Mirage, or Nemesis?Compared to Illich, who was nihilistic about the entire medicalenterprise (“The medical establishment has become a major threat tohealth” (Illich, 1975, p. 3)), Stegenga's ‘medical nihilism’ is a ther-apeutic nihilism, challenging the effectiveness of contemporary medicalinterventions. Should we accept his conclusion? Not quite, I argue. Theproblem is that there is vagueness or imprecision in his central argu-ment, which stands in the way of meaningfully interpreting and eval-uating it. Moreover, the existing meta-research evidence on medicinethat might help make the terms in his argument more precise is notconstraining enough to render it interpretable and evaluable. Thus, wecannot conclude that our confidence in the effectiveness of medicalinterventions – even contemporary pharmaceuticals – ought to be ‘low’.Nonetheless, Stegenga's book succeeds in arguing that we should be lessconfident in the effectiveness of drugs than many of us are, which is asignificant achievement, and demands response both from philosophyof science and from medicine.Stegenga's claim is not the one that we should have low confidencein all medical treatments: some interventions like insulin and anti-biotics are ‘magic bullet's that work very well. Rather, we should havelow confidence in general, in most interventions. One could rightlyquestion whether Stegenga's nihilistic conclusion is the most helpfulone to draw given that it does not tell us how to more reliably assess ourconfidence in interventions or indeed which interventions are magicand which are muck (but as we will see in a moment, Stegenga's centralarguments do address these issues). However, here I scrutinize whetherhis main conclusion is warranted by his arguments.One could question the wide scope of Stegenga's therapeutic nihi-lism. As a medical student, I was struck by the dizzying diversity ofmedical interventions, from surgeries and vaccines to psychotherapyand plain old medical counseling and advice. There is a diversity ofkinds of evidence to go along with this diversity of treatments, in-cluding evidence from clinical experience, basic science and observa-tional epidemiology (these kinds of evidence have their own problems).As Broadbent (2019) points out, Stegenga's criticisms of medical re-search best apply to pharmaceuticals studied in clinical trials. I agree,but here I challenge Stegenga's conclusion even if we qualify it as anarrower thesis about contemporary pharmaceuticals.Stegenga uses Bayes’ Rule to organize his Master Argument formedical nihilism:=p(H|E) p(E|H) x p(H)p(E)H is a hypothesis claiming that a medical intervention is effective.The evidence E is best understood as the apparent finding of a smalleffect size in a clinical trial or meta-analysis of trials. The conclusion ofthe Master Argument is that the probability of H given E, p(H|E), isgenerally low: “on average we ought to have a low posterior probabilityin that hypothesis, p(H|E) – in short, medical nihilism is compelling”(Stegenga, 2018, p. 168). The chapter-level arguments of Stegenga'sbook are meant to provide justification for thinking that the priorprobability of H (or p(H)) is ‘low’, the probability of E (or p(E)) is ‘high’,and the probability of E given H (or p(E|H)) is ‘low’; and therefore, byBayes' Rule, p(H|E) is ‘low’.p(H) is low because the pathophysiologic basis of most diseases iscomplex (we should expect few interventions to be magic bullets thatfly right to the heart of the disease and target it successfully with fewside effects) and historically many medical interventions in use at somepoint have later been rejected as ineffective. p(E) is high because ofwidespread research bias: clinical trials and meta-analysis are malleableand vested interests tune the methods to get a positive finding; thus, theprobability of finding a small positive result is high. Finally, p(E|H) islow because most apparent effect sizes measured in trials are low (lowenough that we should actually attribute them to bias rather than anyeffectiveness of the intervention) and because studies are frequentlydiscordant, contradicting each other – if most interventions were trulyeffective, these findings would be uncommon.There is vagueness or imprecision in Stegenga's conclusion that ourconfidence in the effectiveness of medical interventions should be ‘low’.What is meant by ‘low’? My quantitation of ‘low’ might overlap sub-stantially with your quantitation of ‘high’ (a fact that has led the USIntelligence Community to recommend the use of numbers in statingthe probability of politically relevant hypotheses (Tetlock & Gardner,2015)). This problem makes the implications of Stegenga's conclusiondifficult to discern. Stegenga argues that “there can be no precise orgeneral answer” to how low is ‘low’, but: “It is enough to say: lower,often much lower, than our confidence on average now appears to be”,and he argues that our present confidence is high (p. 183). While thesuggestion that our present confidence is inflated is important, ashttps://doi.org/10.1016/j.shpsc.2020.101270Received 22 January 2020; Accepted 21 February 2020Studies in History and Philosophy of Biol & Biomed Sci 81 (2020) 1012701369-8486Stegenga suggests it does not put a precise number to his medical ni-hilism. Sometimes, a low confidence is enough to justify using a drug,depending on what is to be gained or lost – it depends on how low is‘low’.However, I already said that I would not focus on how helpful histhesis is, but rather whether it is warranted. The problem of imprecisionI want to explore further is vagueness in the other terms of Stegenga'sargument: p(H), p(E) and p(E|H). Stegenga writes, “I do not pretendthat the terms in the master argument can be precisely quantified, ei-ther generally or for a particular medical intervention” (p. 178). Yetimprecision makes it challenging to evaluate Stegenga's arguments foreach term. The fact that many medical interventions are eventuallyrejected and that the pathophysiology of many diseases is complex doesnot allow us to conclude that p(H) is low until we have a clear idea ofwhat ‘low’ means (and how common these phenomena are – the pa-thophysiologic rationale for biologics such as monoclonal antibodiesmay be stronger than the rationale for antidepressants or deep brainstimulation). Even then it is challenging to draw a definite conclusion.Moreover, imprecision makes it difficult to evaluate the overallMaster Argument. Stegenga notes that the Master Argument is validbecause it makes use of Bayes' Rule. But Bayes Rule is an equation, andthe conclusion of the Master Argument only follows validly if we choosecertain numerical values for ‘low’ and ‘high’. To illustrate, if by ‘low’probability we mean 0.5 and by ‘high’ probability we mean 0.25, thenplugging in these numbers into Bayes' Rule we get a p(H|E) of 1.0,which does not meet our definition of ‘low’. Though it might seemstrange to consider 0.25 to be high and 0.5 to be low, ‘high’ and ‘low’are being applied to different types of phenomena, so it might be rea-sonable to say that a 25% rate of bias among clinical studies is highwhile a 50% prior confidence in the hypothesis is low.Adding precision to the terms of the Master Argument would makeit more interpretable and evaluable. But then a tricky question presents:what numerical values for the terms of the Master Argument are war-ranted? Stegenga often discusses relevant evidence of wide trends inmedical research, including industry bias, publication bias and dis-cordance among studies. As this kind of research is relevant to assessingeffectiveness, I have called it ‘meta-research evidence’ and argued thatit should be used in evaluating therapies (Fuller, 2018). Meta-researchevidence provides a promising avenue for providing quantitative con-straints on the terms of the Master Argument and helping us betterassess just how confident in the effectiveness of medical interventionswe should be.1Stegenga argues that the historical record of medical interventionsthat were once accepted and later rejected as ineffective providessupport for a low prior probability of effectiveness, but adds that thisphenomenon lacks a quantitative analysis. Prasad et al. (2013) analyzedall studies of medical practices published in the New England Journal ofMedicine in the first decade of the Twenty-First Century. Of 363 tests ofan existing standard medical practice, 146 (40%) found it worse than aprevious standard or than doing nothing (‘medical reversal’), 138 (38%)confirmed the standard and 79 (22%) were inconclusive. In otherwords, half of all conclusive tests were reversals, which might suggestan estimate for the frequency of untested interventions that are effec-tive of 0.5 – the prior probability of effectiveness before testing. How-ever, this 0.5 may not provide a reasonable estimate for p(H) for manyreasons, including: the medical practices analyzed by Prasad et al. werenot exclusively treatments, tests are not always correct, and the sampleof practices studied might not be representative of all medical practices(excluding, for instance, newer or experimental interventions).Turning to the probability of the evidence given the hypothesis, p(E|H), Stegenga notes that clinical trials and meta-analyses of the sameintervention often contradict one another. He cites meta-research byIoannidis (2005) showing that 14/34 (41%) of positive highly citedtherapeutic studies that were compared with a second study of the sameintervention were contradicted by the second study in terms of thequalitative (20.5%) or quantitative (20.5%) treatment effect. There aremany meta-research studies of contradictions in the medical literature,some of them involving large numbers of interventions in the Cochranedatabase (e.g. Furukawa, Guyatt, & Griffith, 2002; Pereira, Horwitz, &Ioannidis, 2012; Pereira & Ioannidis, 2011). However, none of thesestudies pretend to include a representative sample of interventions, andthere are many possible explanations when two studies disagree, not allof which suggest problems with the evidence (Fuller, 2018).A further consideration Stegenga raises for determining the p(E|H)is that many apparent therapeutic effect sizes are small. He cites someexamples of effect sizes listed on NNT.com. The number-needed-to-treat(NNT) is a measure of effect size, the number of people one would needto treat with the intervention to prevent or cause one outcome. I did abit of meta-research of my own, and found that of the 70 unique in-terventions on NNT.com (most of them pharmaceuticals) deemed to beeffective with listed NNTs, 35 (50%) had an NNT greater than 10, and53 (76%) had an NNT greater than 5 (The NNT Group, 2019). Un-fortunately, it is difficult to discern the relevance of these data becauseit is unclear how small an apparent effect size must be if we are toplausibly attribute it to bias.2Finally, in quantifying the rate of bias among therapeutic studiesand the probability of the evidence, p(E), several kinds of meta-researchthat Stegenga discusses are relevant. For instance, a Cochrane review byLundh, Lexchin, Mintzes, Schroll, and Bero (2017) suggests that in-dustry-funded drug and device studies are 1.27 times more likely to bepositive compared to non-industry-funded studies. Much of Stegenga'sanalysis of medical research provides a convincing reason to believethat this association is due to bias (‘industry bias’). Thus, we couldestimate that the results of at least 27 of every 127 (21%) positive in-dustry-funded trials are due to bias - that's 14% of all industry-fundedtrials included in the review. Other meta-research evidence such asevidence of widespread publication bias (Hopewell, Loudon, Clarke,Oxman, & Dickersin, 2009) suggests that the probability of bias mightbe higher than 14% but does not narrow the probability of bias on amore precise value. Moreover, the probability of positive evidence Edepends on more than just the probability of bias.In summary, the available meta-research evidence is not sufficientfor estimating numeric values for the terms of Stegenga's MasterArgument. On the other hand, the values he assigns to them (‘low’,‘high’) are too imprecise to evaluate whether they are warranted andwhether they in turn warrant the conclusion, ‘medical nihilism’.However, Stegenga's arguments do succeed in showing that we shouldbe less confident in the effectiveness of drugs than many of us are.More meta-research could be useful for further constraining thesevalues and evaluating Stegenga's therapeutic nihilism. However, thereis a more urgent reason for wanting this kind of evidence: for thepurpose of re-evaluating individual therapies (Fuller, 2018), especiallyconsidering the problems that Stegenga explores masterfully in hisbook. Stegenga's rigorous defense of his position in Medical Nihilismshould temper any naïve medical optimism. To that end, it is an im-portant antidote, even if the precise dose is difficult to titrate.AcknowledgmentsThanks to Alex Broadbent, Jacob Stegenga and Sean Valles forhelpful feedback and fruitful discussion of earlier drafts.1 Elsewhere, Stegenga (2011) provides a strong critique of meta-analysis, atool often used in meta-research.2 In addition to 82 interventions listed as effective, NNT.com lists 47 inter-ventions as ineffective on net, 16 interventions as harmful on net, and 39 in-terventions as lacking sufficient evidence to make a determination.Book Forum Studies in History and Philosophy of Biol & Biomed Sci 81 (2020) 1012702ReferencesBroadbent, A. (2019). Philosophy of medicine. New York: Oxford University Press.Foucault, M. (1965). Madness and civilization: A history of insanity in the age of reason. NewYork: Random House, Inc.Fuller, J. (2018). Meta-research evidence for evaluating therapies. Philosophy of Science,85, 767–780.Furukawa, T. A., Guyatt, G. H., & Griffith, L. E. (2002). Can we individualize the 'numberneeded to treat'? An empirical study of summary effect measures in meta-analyses.International Journal of Epidemiology, 31(1), 72–76.Hopewell, S., Loudon, K., Clarke, M. J., Oxman, A. D., & Dickersin, K. (2009). Publicationbias in clinical trials due to statistical significance or direction of trial results.Cochrane Database of Systematic Reviews, 1, Mr000006. https://doi.org/10.1002/14651858.MR000006.pub3.Illich, I. (1975). Medical Nemesis: The expropriation of health. London: Calder & Boyars Ltd.Ioannidis, J. P. A. (2005). Contradicted and initially stronger effects in highly citedclinical research. Journal of the American Medical Association, 294(2), 218–228.Lundh, A., Lexchin, J., Mintzes, B., Schroll, J. B., & Bero, L. (2017). Industry sponsorshipand research outcome. Cochrane Database of Systematic Reviews, 2, Mr000033.https://doi.org/10.1002/14651858.MR000033.pub3.McKeown, T. (1980). The Role of medicine: Dream, mirage, or Nemesis? Princeton:Princeton University Press.Pereira, T. V., Horwitz, R. I., & Ioannidis, J. P. (2012). Empirical evaluation of very largetreatment effects of medical interventions. Jama, 308(16), 1676–1684.Pereira, T. V., & Ioannidis, J. P. (2011). Statistically significant meta-analyses of clinicaltrials have modest credibility and inflated effects. Journal of Clinical Epidemiology,64(10), 1060–1069.Prasad, V., Vandross, A., Toomey, C., Cheung, M., Rho, J., Quinn, S., ... Cifu, A. (2013). Adecade of reversal: An analysis of 146 contradicted medical practices. Mayo ClinicProceedings, 88(8), 790–798.Stegenga, J. (2011). Is meta-analysis the platinum standard of evidence? Studies in Historyand Philosophy of Biological and Biomedical Sciences, 42(4), 497–507.Stegenga, J. (2018). Medical nihilism. Oxford: Oxford University Press.Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The art and science of prediction. NewYork: Broadway Books.The NNT Group (2019). Therapy (NNT) reviews by rating. http://www.thennt.com/home-nnt/, Accessed date: 5 May 2019.Wootton, D. (2007). Bad medicine: Doctors doing harm since hippocrates. Oxford: OxfordUniversity Press.Jonathan Fuller,Department of History and Philosophy of Science, University of Pittsburgh,Pittsburgh, PA, USAE-mail address: JPF53@pitt.edu.Book Forum Studies in History and Philosophy of Biol & Biomed Sci 81 (2020) 1012703

Medical Nihilism by Jacob Stegenga: What is the right dose?

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