Skip to main content
SpringerLink
Log in

Mechanisms and the Evidence Hierarchy

Topoi volume 33, pages 339–360 (2014)Cite this article

Abstract

Evidence-based medicine (EBM) makes use of explicit procedures for grading evidence for causal claims. Normally, these procedures categorise evidence of correlation produced by statistical trials as better evidence for a causal claim than evidence of mechanisms produced by other methods. We argue, in contrast, that evidence of mechanisms needs to be viewed as complementary to, rather than inferior to, evidence of correlation. In this paper we first set out the case for treating evidence of mechanisms alongside evidence of correlation in explicit protocols for evaluating evidence. Next we provide case studies which exemplify the ways in which evidence of mechanisms complements evidence of correlation in practice. Finally, we put forward some general considerations as to how the two sorts of evidence can be more closely integrated by EBM.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1

Similar content being viewed by others

Notes

  1. We recognise that hierarchies are capable of playing other roles in facilitating medical decision making too, such as providing safeguards against litigation, or simplifying prescription practices (Timmermans and Berg 2003). However, our focus here is firmly on their epistemological role.

  2. ‘Mechanism-based reasoning’ seems to refer roughly to what we call evidence of mechanisms. Howick writes ‘Mechanistic reasoning is an inferential chain (or web) linking the intervention (such as HRT) with a patient-relevant outcome, via relevant mechanisms.’ (Howick 2011, p. 929).

  3. The International Agency for Research on Cancer (IARC) is one of the few agencies that now tries to systematically consider evidence of mechanisms (IARC 2006, \((\S B.4)\) ). One way it does this is by formulating a two dimensional hierarchy that considers evidence obtained in experimental studies on animals along one dimension and evidence obtained on humans along the other. However, on each dimension the emphasis is still on evidence obtained from statistical trials.

  4. There is an established tradition in sociology and social science on social mechanisms, see for instance the classic text Hedström and Swedberg (1988) and the recent contribution Demeulenaere (2011).

  5. The remainder of this section will deal with the reference class problem as applied to trial design. However, as the epidemiological terminology differs significantly between trial design and interpretation of trial data, it’s worth making a few brief remarks here regarding the problem as it affects interpretation, particularly the practice of stratification. Stratification involves dividing up trial results to examine outcomes in partitions of the trial population thought to be interestingly different from the general population. For instance, a trial of an antihypertensive agent might stratify the trial population into age groups at the data analysis stage, to see if the drug response differs. This kind of practice is limited by various kinds of information bias, including the sparse data problem: the smaller the strata, the greater the variability of apportionment ratios (Rothman et al. 2008), and the lower the precision of any resulting causal claims. In short, the problem is identical in either trial design or interpretation.

  6. And see the literature on sample size calculation for examples of this (Rothman et al. 2008, pp. 149ff).

  7. Howick (2011) provides other examples of such compelling stories and how they led to the development of EBM.

  8. MRC (1948, 1949, 1950) contain the reports on these trials published in the British Medical Journal. There is an overview of the trials in Daniels and Bradford Hill (1952), while Bradford Hill (1990) gives some interesting reminiscences.

  9. In a sense, we follow up the suggestion of Solomon (2011) that EBM largely ignores basic science, particularly mechanisms, and we offer an account of how to integrate grading evidence of mechanisms and grading evidence of correlation.

References

  • Bassler D, Briel M, Montori VM, Lane M, Glasziou P, Zhou Q, Heels-Ansdell D, Walter SD, Guyatt GH, Flynn DN et al (2010) Stopping randomized trials early for benefit and estimation of treatment effects. J Am Med Assoc 303(12):1180–1187

    Article  Google Scholar 

  • Bechtel W (2008) Mental mechanisms: philosophical perspectives on cognitive neuroscience. Routledge, Oxford

    Google Scholar 

  • Bell G (2008) Selection: the mechanism of evolution, 2nd edn. OUP

  • Bem DJ (2011) Feeling the future: experimental evidence for anomalous retroactive influences on cognition and affect. J Pers Soc Psychol 100:407–425

    Article  Google Scholar 

  • Bernard C (1856) An introduction to the study of experimental medicine. Macmillan, New York (1927 edition)

  • Bradford Hill A (1965) The environment and disease: association or causation? Proc R Soc Med 58:295–300

    Google Scholar 

  • Bradford Hill A (1990) Memories of the British streptomycin trial. Controlled Clin Trials 2:77–79

    Article  Google Scholar 

  • Broadbent A (2011) Inferring causation in epidemiology: mechanisms, black boxes, and contrasts. In: Illari P, Russo F, Williamson J (eds) Causality in the sciences. Oxford University Press, Oxford, pp 45–69

    Chapter  Google Scholar 

  • Brock TD (1988) Robert Koch: a life in medicine and bacteriology, 2 edn. ASM Press

  • Campaner R (2011) Understanding mechanisms in the health sciences. Theor Med Bioethics 32:5–17

    Article  Google Scholar 

  • Campaner R, Galavotti MC (2012) Evidence and the assessment of causal relations in the health sciences. Int Stud Philos Sci 26(1):27–45

    Article  Google Scholar 

  • Carter KC (2012) The decline of therapeutic bloodletting and the collapse of traditional medicine. Transaction Publishers, New Brunswick

    Google Scholar 

  • Cartwright N (2007) Causal powers: what are they? why do we need them? what can be done with them and what cannot? LSE discussion paper. http://www.lse.ac.uk/CPNSS/projects/CoreResearchProjects/ContingencyDissentInScience/DP/CausalPowersMonographCartwrightPrint%20Numbers%20Corrected.pdf

  • Cartwright N (2010) What are randomised controlled trials good for? Philos Stud 147:59–70

    Article  Google Scholar 

  • Cartwright N, Hardie J (2012) Evidence based policy: a practical guide to doing it better. OUP, USA

  • Cartwright N, Munro E (2010) The limitations of randomized controlled trials in predicting effectiveness. J Eval Clin Pract 16:260–266

    Article  Google Scholar 

  • Clarke B (2011a) Causality in medicine with particular reference to the viral causation of cancers. PhD thesis. Department of Science and Technology Studies, University College London, London

  • Clarke B (2011b) Causation and melanoma classification. Theor Med Bioethics 32:19–32

    Article  Google Scholar 

  • Clarke B, Gillies D, Illari P, Russo F, Williamson J (2013) The evidence that evidence-based medicine omits. Prev Med. doi:10.1016/j.ypmed.2012.10.020

  • Claveau F (2012) The Russo–Williamson theses in the social sciences: causal inference drawing on two types of evidence. Stud Hist Philos Biol Biomed Sci 43(4):806–813

    Google Scholar 

  • Cook T, Campbell D (1979) Quasi-experimentation: design and analysis issues for field settings. Rand MacNally, Chicago

    Google Scholar 

  • Craver C (2007) Explaining the brain. Clarendon Press, Oxford

    Book  Google Scholar 

  • Cucherat M, Haugh MC, Gooch M, Boissel J-P (2000) Evidence of clinical efficacy of homeopathy: a meta-analysis of clinical trials. Eur J Clin Pharmacol 56:27–33

    Article  Google Scholar 

  • Daniels M, Bradford Hill A (1952) Chemotherapy of pulmonary tuberculosis in young adults. An analysis of the combined results of three medical research council trials. Br Med J 1162–1168

  • Darby G, Williamson J (2011) Imaging technology and the philosophy of causality. Philos Technol 24(2):115–136

    Article  Google Scholar 

  • Darden L (2006) Reasoning in biological discoveries. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Debré P (1994) Louis Pasteur. The John Hopkins University Press. English translation by Elborg Forster, 1998

  • Demeulenaere P (ed) (2011) Analytical sociology and social mechanisms. Cambridge University Press, Cambridge

  • Dragulinescu S (2012) On ‘stabilising’ medical mechanisms, truth-makers and epistemic causality: a critique to Williamson and Russo’s approach. Synthese 187(2):785–800

    Google Scholar 

  • Fayad ZA, Mani V, Woodward M, Kallend D, Abt M, Burgess T, Fuster V, Ballantyne CM, Stein EA, Tardif J-C, Rudd JHF, Farkouh ME, Tawakol A (2011) Safety and efficacy of dalcetrapib on atherosclerotic disease using novel non-invasive multimodality imaging (dal-PLAQUE): a randomised clinical trial. Lancet 378(9802):1547–1559

    Article  Google Scholar 

  • Florey ME (1961) The clinical application of antibiotics. Streptomycin and other antibiotics active Against tuberculosis. vol 2, Oxford University Press, London

    Google Scholar 

  • Fox W, Ellard G, Mitchison D (1999) Studies on the treatment of tuberculosis undertaken by the british medical research council tuberculosis units, 1946–1986, with relevant subsequent publications. Int J Tuberc Lung Dis 3(10s2):S231–S279

    Google Scholar 

  • Gillies DA (2011) The Russo–Williamson thesis and the question of whether smoking causes heart disease. In: Illari PM, Russo F, Williamson J (eds) Causality in the sciences, Oxford University Press, Oxford, pp 110–125

    Chapter  Google Scholar 

  • Hedström P, Swedberg R (1988) Social mechanism: an analytical approach to social theory. Cambridge University Press, Cambridge

    Google Scholar 

  • Hitchcock C (2001) A tale of two effects. Philos Rev 110(3):361–396

    Article  Google Scholar 

  • Howick J (2011) Exposing the vanities—and a qualified defence—of mechanistic evidence in clinical decision-making. Philos Sci 78(5):926–940. Proceedings of the Biennial PSA 2010

    Google Scholar 

  • IARC (2006) IARC monographs on the evaluation of carcinogenic risks to humans: preamble. Int Agency Res Cancer, http://monographs.iarc.fr/ENG/Preamble/CurrentPreamble.pdf

  • Illari PM (2011) Mechanistic evidence: disambiguating the Russo–Williamson thesis. Int Stud Philos Sci 25:139–157

    Article  Google Scholar 

  • Illari PM, Williamson J (2012) What is a mechanism? thinking about mechanisms across the sciences. Eur J Philos Sci 2:119–135

    Article  Google Scholar 

  • Kshirsagar AV, Carpenter M, Bang H, Wyatt SB, Colindres RE (2006) Blood pressure usually considered normal is associated with an elevated risk of cardiovascular disease. Am J Med 119(2):133–141

    Article  Google Scholar 

  • La Caze A (2008) Evidence-based medicine can’t be …. Soc Epistemol 22(4):353–370

    Article  Google Scholar 

  • La Caze A (2009) Evidence-based medicine must be …. J Med Philos 34:509–527

    Article  Google Scholar 

  • La Caze A (2011) The role of basic science in evidence-based medicine. Biol Philos 26(1):81–98

    Article  Google Scholar 

  • La Caze A, Djulbegovic B, Senn S (2012) What does randomisation achieve?. Evid Med 17:1–2

    Article  Google Scholar 

  • Leibovici L (2001) Effects of remote, retroactive intercessory prayer on outcomes in patients with bloodstream infection: randomised controlled trial. Br Med J 323:1450–1451

    Article  Google Scholar 

  • Lucas R (1976) Econometric policy evaluation. In: Brunner K, Meltzer A (eds) The Phillips curve and labor markets, volume 1 of Carnegie–Rochester conference series on public policy. North-Holland, Amsterdam, pp 161–168

  • Machamer P, Darden L, Craver C (2000) Thinking about mechanisms. Philos Sci 67:1–25

    Article  Google Scholar 

  • McLaren L, Ghali LM, Lorenzetti D, Rockl M (2006) Out of context? Translating evidence from the North Karelia project over place and time. Health Educ Res 22(3):414–424

    Article  Google Scholar 

  • MRC (1948) Streptomycin treatment of pulmonary tuberculosis. Br Med J 769–782

  • MRC (1949) Treatment of pulmonary tuberculosis with para-aminosalicylic acid and streptomycin: preliminary report. Br Med J 1521

  • MRC (1950) Treatment of pulmonary tuberculosis with streptomycin and para-amino-salicylic acid. Br Med J 1073–1085

  • Munro SA, Lewin SA, Smith HJ, Engel ME, Fretheim A, Volmink J (2007) Patient adherence to tuberculosis treatment: a systematic review of qualitative research. PLoS Med 4(7):e238

    Article  Google Scholar 

  • NICE (2006) The guidelines manual. National Institute for Health and Clinical Excellence, London. Available from: http://www.nice.org.uk

  • NICE (2009) The guidelines manual. National Institute for Health and Clinical Excellence, London. Available from: http://www.nice.org.uk

  • NICE (2011a) CG117: tuberculosis. National Institute for Health and Clinical Excellence, London. Available from: http://www.nice.org.uk

  • NICE (2011b) CG127: hypertension: full guideline. National Institute for Health and Clinical Excellence, London. Available from: http://www.nice.org.uk

  • NICE (2011c) CG127: hypertension: quick reference guide. National Institute for Health and Clinical Excellence, London. Available from: http://www.nice.org.uk

  • Northcott R (2012) How necessary are randomized controlled trials? In: Munson R (ed) Intervention and reflection: basic issues in medical ethics, 9th edn. Thomson Wadsworth, pp 187–191

  • OCEBM Levels of Evidence Working Group (2011) The Oxford 2011 levels of evidence. Oxford Centre for Evidence-Based Medicine, http://www.cebm.net/index.aspx?o=5653

  • Papineau D (1994) The virtues of randomization. Br J Philos Sci 45:437–450

    Article  Google Scholar 

  • Puska P, Vartiaine E, Laatikainen T, Jousilahti P, Paavola M (eds) (2009) The North Karelia Project: from North Karelia to national action. National Institute for Health and Welfare

  • Reichenbach H (1949) The theory of probability: an inquiry into the logical and mathematical foundations of the calculus of probability. University of California Press, Berkeley, CA

    Google Scholar 

  • Rothman K, Greenland S, Lash T (2008) Modern epidemiology. Lippincott Williams & Wilkins, Philadelphia, PA

    Google Scholar 

  • Russo F, Williamson J (2007) Interpreting causality in the health sciences. Int Stud Philos Sci 21(2):157–170

    Article  Google Scholar 

  • Russo F, Williamson J (2011a) Epistemic causality and evidence-based medicine. History Philos Life Sci 33(4):563–582

    Google Scholar 

  • Russo F, Williamson J (2011b) Generic versus single-case causality: the case of autopsy. Eur J Philos Sci 1(1):47–69

    Article  Google Scholar 

  • Russo F, Williamson J (2012) EnviroGenomarkers: the interplay between mechanisms and difference making in establishing causal claims. Med Stud 3(4):249–262

    Article  Google Scholar 

  • Sackett DL, Rosenberg WMC, Gray JAM, Haynes RB, Richardson WS (1996) Evidence based medicine: what it is and what it isn’t. Br Med J 312(7023):71–72

    Article  Google Scholar 

  • Salmon WC (1977) Objectively homogeneous reference classes. Synthese 36(4):399–414

    Article  Google Scholar 

  • Salmon WC, Jeffrey RC, Greeno JG (1971) Statistical explanation and statistical relevance. University of Pittsburgh Press, Pittsburgh, PA

    Google Scholar 

  • Schwartz GG, Olsson AG, Abt M, Ballantyne CM, Barter PJ, Brumm J, Chaitman BR, Holme IM, Kallend D, Leiter LA, Leitersdorf E, McMurray JJ, Mundl H, Nicholls SJ, Shah PK, Tardif J-C, Wright RS (2012) Effects of dalcetrapib in patients with a recent acute coronary syndrome. N Engl J Med 367(22):2089–2099. PMID: 23126252

    Google Scholar 

  • Sober E (1988) The principle of the common cause. In: Fetzer JH (ed) Probability and causality: essays in honour of Wesley C. Salmon. Reidel, Dordrecht, pp 211–228

  • Solomon M (2011) Just a paradigm: evidence-based medicine in epistemological context. Eur J Philos Sci 1:451–466

    Article  Google Scholar 

  • Steel D (2008) Across the boundaries: extrapolation in biology and social science. Oxford University Press, Oxford

    Google Scholar 

  • Stegenga J (2011) Is meta-analysis the platinum standard of evidence. Stud History Philos Biol Biomed Sci 42:497–507

    Article  Google Scholar 

  • Stein EA, Roth EM, Rhyne JM, Burgess T, Kallend D, Robinson JG (2010) Safety and tolerability of dalcetrapib (RO4607381/JTT-705): results from a 48-week trial. Eur Heart J 31(4):480–488

    Article  Google Scholar 

  • Thompson RP (2011) Causality, theories and medicine. In: Illari PM, Russo F, Williamson J (eds) Causality in the sciences. Oxford University Press, Oxford, pp 25–44

    Chapter  Google Scholar 

  • Timmermans S, Berg M (2003) The gold standard: the challenge of evidence-based medicine and standardization in health care. Temple University Press, Philadelphia

    Google Scholar 

  • Victora CG, Habicht J-P, Bryce J (2004) Evidence-based public health: moving beyond randomized trials. Am J Public Health 94:400–405

    Article  Google Scholar 

  • Wagner EH (1982) The North Karelia Project: what it tells us about the prevention of cardiovascular disease. Am J Public Health 72(1):51–53

    Article  Google Scholar 

  • Weber E (2009) How probabilistic causation can account for the use of mechanistic evidence. Int Stud Philos Sci 23(3):277–295

    Article  Google Scholar 

  • Williamson J (2005) Bayesian nets and causality: philosophical and computational foundations. Oxford University Press, Oxford

    Google Scholar 

  • Williamson J (2013) How can causal explanations explain? Erkenntnis. doi:10.1007/s10670-013-9512-x

  • Worrall J (2002) What evidence in evidence-based medicine. Philos Sci 69:S316–S330

    Article  Google Scholar 

  • Worrall J (2007) Why there’s no cause to randomize. Br J Philos Sci 58:451–488

    Article  Google Scholar 

  • Worrall J (2010) Evidence: philosophy of science meets medicine. J Eval Clin Pract 16:356–362

    Article  Google Scholar 

Download references

Acknowledgments

We thank the UK Arts and Humanities Research Council for supporting this research. F. Russo also acknowledges financial support from the FWO-Flanders (2012–2013) as Pegasus Marie Curie Fellow. We are extremely grateful to the very many people who came to various events we organised during 2012, and participated in the discussions that allowed us to develop these ideas. We owe particular thanks to Ian McKay, Barbara Osimani, Jacob Stegenga and David Teira for extensive comments leading to significant improvements to the paper.

Author information

Authors and Affiliations

  1. Science and Technology Studies, University College London, London, UK

    Brendan Clarke, Donald Gillies & Phyllis Illari

  2. Dipartimento di Studi Umanistici, Università degli Studi di Ferrara, Ferrara, Italy

    Federica Russo

  3. Philosophy, SECL, University of Kent, Canterbury, UK

    Jon Williamson

Authors
  1. Brendan Clarke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Donald Gillies
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Phyllis Illari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Federica Russo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jon Williamson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Phyllis Illari.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Clarke, B., Gillies, D., Illari, P. et al. Mechanisms and the Evidence Hierarchy. Topoi 33, 339–360 (2014). https://doi.org/10.1007/s11245-013-9220-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11245-013-9220-9

Keywords

search

Navigation