Phyllis Illari

The evidence that evidence-based medicine omits

According to current hierarchies of evidence for EBM, evidence of correlation (e.g., from RCTs) is always more important than evidence of mechanisms when evaluating and establishing causal claims. We argue that evidence of mechanisms needs to be treated alongside evidence of correlation. This is for three reasons. First, correlation is always a fallible indicator of causation, subject in particular to the problem of confounding; evidence of mechanisms can in some cases be more important than evidence of correlation when assessing a causal claim. Second, evidence of mechanisms is often required in order to obtain evidence of correlation (for example, in order to set up and evaluate RCTs). Third, evidence of mechanisms is often required in order to generalise and apply causal claims. While the EBM movement has been enormously successful in making explicit and critically examining one aspect of our evidential practice, i.e., evidence of correlation, we wish to extend this line of work to make explicit and critically examine a second aspect of our evidential practices: evidence of mechanisms.

A Radical Approach to Ebola: Saving Humans and Other Animals

As the usual regulatory framework did not fit well during the last Ebola outbreak, innovative thinking still needed. In the absence of an outbreak, randomised controlled trials of clinical efficacy in humans cannot be done, while during an outbreak such trials will continue to face significant practical, philosophical, and ethical challenges. This article argues that researchers should also test the safety and effectiveness of novel vaccines in wild apes by employing a pluralistic approach to evidence. There are three reasons to test vaccines in wild populations of apes: i) protect apes; ii) reduce Ebola transmission from wild animals to humans; and iii) accelerate vaccine development and licensing for humans. Data obtained from studies of vaccines among wild apes and chimpanzees may even be considered sufficient for licensing new vaccines for humans. This strategy will serve to benefit both wild apes and humans.

IQ: Purpose and Dimensions

In this article I examine the problem of categorising dimensions of information quality (IQ), against the background of a serious engagement with the hypothesis that IQ is purpose-dependent. First, I examine some attempts to offer categories for IQ, and a specific problem that impedes convergence in such categorisations is diagnosed. Based on this new understanding, I suggest a new way of categorising both IQ dimensions and the metrics used in implementation of IQ improvement programmes according to what they are properties of. I conclude the paper by outlining an initial categorisation of some IQ dimensions and metrics in standard use to illustrate the value of the approach.

Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation

Abstract In the last decades, Systems Biology (including cancer research) has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a) causality in epidemiology and in philosophical theorizing—notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b) new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c) the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations) and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social—are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.

Information Quality, Data and Philosophy

In this opening chapter, we review the literature on information quality (IQ). Our major aim is to introduce the issues, and trace some of the history of the debates, with a view to situating the chapters in this volume – whose authors come from different disciplines – to help make them accessible to readers with different backgrounds and expertise. We begin in this section by tracing some influential analyses of IQ in computer science. This is a useful basis for examining some examples of developing work on IQ in section two. We look at some cases for applying IQ in section three, and conclude with some discussion points in section four.

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.