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.
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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.
‘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).
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.
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.
And see the literature on sample size calculation for examples of this (Rothman et al. 2008, pp. 149ff).
Howick (2011) provides other examples of such compelling stories and how they led to the development of EBM.
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.
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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.
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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
- Evidence of mechanism
- Evidence in medicine
- Evidence-based medicine