Veli-Pekka Parkkinen

Evaluating Evidence of Mechanisms in Medicine: Principles and Procedures

This book is the first to develop explicit methods for evaluating evidence of mechanisms in the field of medicine. It explains why it can be important to make this evidence explicit, and describes how to take such evidence into account in the evidence appraisal process. In addition, it develops procedures for seeking evidence of mechanisms, for evaluating evidence of mechanisms, and for combining this evaluation with evidence of association in order to yield an overall assessment of effectiveness. n nEvidence-based medicine seeks to achieve improved health outcomes by making evidence explicit and by developing explicit methods for evaluating it. To date, evidence-based medicine has largely focused on evidence of association produced by clinical studies. As such, it has tended to overlook evidence of pathophysiological mechanisms and evidence of the mechanisms of action of interventions. n nThe book offers a useful guide for all those whose work involves evaluating evidence in the health sciences, including those who need to determine the effectiveness of health interventions and those who need to ascertain the effects of environmental exposures.

The use of mechanistic evidence in drug approval

Abstract The role of mechanistic evidence tends to be under‐appreciated in current evidence‐based medicine (EBM), which focusses on clinical studies, tending to restrict attention to randomized controlled studies (RCTs) when they are available. The EBM+ programme seeks to redress this imbalance, by suggesting methods for evaluating mechanistic studies alongside clinical studies. Drug approval is a problematic case for the view that mechanistic evidence should be taken into account, because RCTs are almost always available. Nevertheless, we argue that mechanistic evidence is central to all the key tasks in the drug approval process: in drug discovery and development; assessing pharmaceutical quality; devising dosage regimens; assessing efficacy, harms, external validity, and cost‐effectiveness; evaluating adherence; and extending product licences. We recommend that, when preparing for meetings in which any aspect of drug approval is to be discussed, mechanistic evidence should be systematically analysed and presented to the committee members alongside analyses of clinical studies.

Extrapolation and the Russo–Williamson thesis

A particular tradition in medicine claims that a variety of evidence is helpful in determining whether an observed correlation is causal. In line with this tradition, it has been claimed that establishing a causal claim in medicine requires both probabilistic and mechanistic evidence. This claim has been put forward by Federica Russo and Jon Williamson. As a result, it is sometimes called the Russo–Williamson thesis. In support of this thesis, Russo and Williamson appeal to the practice of the International Agency for Research on Cancer (IARC). However, this practice presents some problematic cases for the Russo–Williamson thesis. One response to such cases is to argue in favour of reforming these practices. In this paper, we propose an alternative response according to which such cases are in fact consistent with the Russo–Williamson thesis. This response requires maintaining that there is a role for mechanism-based extrapolation in the practice of the IARC. However, the response works only if this mechanism-based extrapolation is reliable, and some have argued against the reliability of mechanism-based extrapolation. Against this, we provide some reasons for believing that reliable mechanism-based extrapolation is going on in the practice of the IARC. The reasons are provided by appealing to the role of robustness analysis.

Robustness and evidence of mechanisms in early experimental atherosclerosis research.

This article considers the evaluation of experimental evidence for a causal relation between cholesterol and atherosclerosis from the beginning of the 1900s until the late 1950s. It has been argued that the medical community failed to see the implications of this early research, and at first unjustifiably rejected a causal link between cholesterol and atherosclerosis. This article argues to the contrary that the medical community was justified to conclude based on the experimental evidence that cholesterol (dietary or blood) is probably not an effective target for preventive treatment. However, the evidence would have been sufficient to ascribe to cholesterol a contributing causal role in atherosclerotic heart disease. This view is argued for based on a rational reconstruction of the researchers evaluation of evidence, specifically, the robustness of evidence for a manipulable dependence between cholesterol and atherosclerosis on the one hand, and the evidence for a mediating mechanism on the other. The case study is used to illustrate that robustness is a feasible methodological principle even when evidence is discordant, and evidence of mechanism should be evaluated on a par with evidence of statistical dependence in establishing causal claims.

Causation in evidence‐based medicine: in reply to Kerry et al.

Kerry et al. criticize our discussion of causal knowledge in evidence-based medicine (EBM) and our assessment of the relevance of their dispositionalist ontology for EBM. Three issues need to be addressed in response: (1) problems concerning transfer of causal knowledge across heterogeneous contexts; (2) how predictions about the effects of individual treatments based on population-level evidence from RCTs are fallible; and (3) the relevance of ontological theories like dispositionalism for EBM.Kerry et al. criticize our discussion of causal knowledge in evidence-based medicine (EBM) and our assessment of the relevance of their dispositionalist ontology for EBM. Three issues need to be addressed in response: (1) problems concerning transfer of causal knowledge across heterogeneous contexts; (2) how predictions about the effects of individual treatments based on population-level evidence from RCTs are fallible; and (3) the relevance of ontological theories like dispositionalism for EBM.

An Introduction to Mechanisms

This chapter offers a brief summary of mechanisms, as including complex-system mechanisms (a complex arrangement of entities and activities, organised in such a way as to be regularly or predictably responsible for the phenomenon to be explained) and mechanistic processes (a spatio-temporal pathway along which certain features are propagated from the starting point to the end point). The chapter emphasises that EBM+ is concerned with evidence of mechanisms, not mere just-so stories, and summarises some key roles assessing evidence of mechanisms can play, particularly with respect to assessing efficacy and external validity.

Using Evidence of Mechanisms to Evaluate Efficacy and External Validity

Previous chapters in Part III develop accounts of how to gather and evaluate evidence of claims about mechanisms. This chapter explains how this evaluation can be combined with an evaluation of evidence for relevant correlations in order to produce an overall evaluation of a causal claim. The procedure is broken down to address efficacy, external validity, and then the overall presentation of the claim.

Evaluating Evidence of Mechanisms

In this chapter, we discuss how to evaluate evidence of mechanisms. This begins with an account of how a mechanistic study provides evidence for features of specific mechanism hypotheses, laying out a three step procedure of evaluating: (1) the methods used, (2) the implementation of the methods, and (3), the stability of the results. The next step is to combine those evaluations to present the quality of evidence of the general mechanistic claim.

Gathering Evidence of Mechanisms

In this chapter we put forward more theoretical proposals for gathering evidence of mechanisms. Specifically, the chapter covers the identification of a number of mechanism hypotheses, formulation of review questions for search, and then how to refine and present the resulting evidence. Key issues include increased precision concerning the nature of the hypothesis being examined, attention to differences between the study population (or populations) and the target population of the evidence assessors, and being alert for masking mechanisms, which are other mechanisms which may mask the action of the mechanism being assessed. An outline example concerning probiotics and dental caries is given. (Databases that may be helpful for some searches can be found online in Appendix A).

Particularisation to an Individual

In Sect. 7.1, we discussed extrapolation from a study population to a target population. In this chapter, we treat particularisation from a study population to one of its members. In both cases, evidence of similarity of mechanisms plays a crucial role.