Topoi

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We are All Bayesian, Everyone is Not a Bayesian

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Abstract

Medical research makes intensive use of statistics in order to support its claims. In this paper we make explicit an epistemological tension between the conduct of clinical trials and their interpretation: statistical evidence is sometimes discarded on the basis of an (often) underlined Bayesian reasoning. We suggest that acknowledging the potentiality of Bayesian statistics might contribute to clarify and improve comprehension of medical research. Nevertheless, despite Bayesianism may provide a better account for scientific inference with respect to the standard frequentist approach, Bayesian statistics is rarely adopted in clinical research. The main reason lies in the supposed subjective elements characterizing this perspective. Hence, we discuss this objection presenting the so-called Reference analysis, a formal method which has been developed in the context of objective Bayesian statistics in order to define priors which have a minimal or null impact on posterior probabilities. Furthermore, according to this method only available data are relevant sources of information, so that it resists the most common criticisms against Bayesianism.

Keywords

Evidence Clinical trials Scientific inference Frequentism Bayesian statistics Reference analysis 

Notes

Acknowledgements

A. Oldofredi is grateful to the Swiss National Science Foundation for financial support (Grant No. 105212-175971).

References

  1. Astin JA, Harkness E, Ernst E (2001) Distant healing. Ann Intern Med 132(6):903–910Google Scholar
  2. Bernardo JM (1997) Non-informative priors do not exist. J Stat Plan Inference 65:159–189 (with discussion)CrossRefGoogle Scholar
  3. Bernardo JM (2003) Bayesian statistics. In: Viertl R (ed) Probability and statistics. Encyclopedia of life support systems (EOLSS). UNESCO, OxfordGoogle Scholar
  4. Bernardo JM, Ramon JM (1998) An introduction to Bayesian reference analysis: inference of the ratio of multinomial parameters. J R Stat Soc Ser D 45:1–35Google Scholar
  5. Cartwright N (2007) Are RCT the gold standard? BioSocieties 1:11–20CrossRefGoogle Scholar
  6. Cartwright N (2010) What are randomised controlled trials good for? Philos Stud 147(1):59–70CrossRefGoogle Scholar
  7. Clarke B, Gilles D, Illari P, Russo F, Williamson J (2014) Mechanism and the evidence hierarchy. Topoi 33(2):339–360CrossRefGoogle Scholar
  8. Douglas H (2009) Science, policy, and the value-free ideal. University of Pittsburgh Press, PittsburghCrossRefGoogle Scholar
  9. Efron B (1986) Why isn’t everyone a Bayesian? Am Stat 40(1):1–5Google Scholar
  10. Emslie G, Rush A, Weinberg W, Kowatch R, Hughes C, Camody T, Rintelmann J (1997) A double-blind, randomized, placebo-controlled trial of fluoxetine in children and adolescents with depression. JAMA Psychiatry 54(11):1037–1037Google Scholar
  11. Gelman A, Hennig C (2017) Beyond subjective and objective in statistics. J R Stat Soc Ser A 180:1–31CrossRefGoogle Scholar
  12. Jack Lee J, Chu CT (2012) Bayesian clinical trials in action. Stat Med 31(25):2955–2972CrossRefGoogle Scholar
  13. Jaynes E (1968) Prior probabilities. IEEE Trans Syst Sci Cyber 4:227–291CrossRefGoogle Scholar
  14. Jeffreys H (1961) Theory of Probability, 3rd edn. Oxford University Press, OxfordGoogle Scholar
  15. Kaptchuk T (2001) Distant healing. Ann Intern Med 134(6):532–533CrossRefGoogle Scholar
  16. Kass R, Wasserman L (1996) The selection of prior distributions by formal rules. J Am Stat Assoc 91(431):1343–1370CrossRefGoogle Scholar
  17. Kincaid H, Dupré J, Wylie A (2007) Value-free science: ideals and illusions?. Oxford University Press, OxfordCrossRefGoogle Scholar
  18. Leibovici L (2011) Effects of remote, retroactive intercessory prayer on outcomes in patients with bloodstream infection: randomised controlled trial. Br Med J 323:1450CrossRefGoogle Scholar
  19. Moyé LA (2008) Bayesians in clinical trials: asleep at the switch. Stat Med 27(4):469–482CrossRefGoogle Scholar
  20. Porter TM (1996) Trust in numbers: the pursuit of objectivity in science and public life. Princeton University Press, PrincetonCrossRefGoogle Scholar
  21. Soofi E (1994) Capturing the intangible concept of information. J Am Stat Assoc 89(428):1243–1254CrossRefGoogle Scholar
  22. Sprenger I (2017) The objectivity of subjective Bayesianism, pp 1 – 27. http://philsci-archive.pitt.edu
  23. Stegenga J (2011) Is meta-analysis the platinum standard for evidence? Stud Hist Philos Sci C 42(4):497–507Google Scholar
  24. Syversveen AR (1998) Non-informative Bayesian priors. Interpretation and problems with construction and applications. Preprint Stat 3:1–11Google Scholar
  25. Teira D (2011) Frequentist versus Bayesian clinical trials. Handbook of the philosophy of science. Philos Med 16:255–298CrossRefGoogle Scholar
  26. Teira D, Reiss J (2013) Causality, impartiality and evidence-based policy. In: Chao H-K, Chen S-T, Millstein R (eds) Mechanism and causality in biology and economics. Springer, New York, pp 207–224CrossRefGoogle Scholar
  27. Vandenbroucke J (2005) Homeopathy and the growth of truth. Lancet 366(9487):691–692CrossRefGoogle Scholar
  28. Whittington C, Kendall T, Fonagy P, Cottrell D, Cotgrove A, Boddington E (2004) Selective serotonin reuptake inhibitors in childhood depression: systematic review of published versus unpublished data. Lancet 363(9418):1341–1345CrossRefGoogle Scholar
  29. Wijeysundera D, Austin P, Hux J, Beattle W, Laupacis A (2009) Bayesian statistical inference enhances the interpretation of contemporary randomized controlled trials. J Clin Epidemiol 62(1):13–21CrossRefGoogle Scholar
  30. Worrall J (2010) Evidence: philosophy of science meets medicine. J Eval Clin Pract 16(2):356–362CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Experimental OncologyEuropean Institute of OncologyMilanItaly
  2. 2.Department of PhilosophyUniversité de LausanneChamberonne, LausanneSwitzerland

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