Evidence-Based Medicine

Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application. Study Design and Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC. Results: We find that concerns regarding explainability are (...) not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating unexplainable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine. Conclusion: Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems. (shrink)

Experimental balance is usually understood as the control for the value of the conditions, other than the one under study, which are liable to affect the result of a test. We will discuss three different approaches to balance. ‘Millean balance’ requires to identify and equalize ex ante the value of these conditions in order to conduct solid causal inferences. ‘Fisherian balance’ measures ex post the influence of uncontrolled conditions through the analysis of variance. In ‘efficiency balance’ the value of the (...) antecedent conditions is decided ex ante according to the efficiency they yield in the estimation of the treatment outcome. Against some old arguments by John Worrall, we will show that in both Fisherian and efficiency balance there are good reasons to randomize the allocation of treatments, in particular when there is no agreement among experimenters as to the antecedent conditions to be controlled for. (shrink)

In this paper, we illustrate some serious difficulties involved in conveying information about uncertain risks and securing informed consent for risky interventions in a clinical setting. We argue that in order to secure informed consent for a medical intervention, physicians often need to do more than report a bare, numerical probability value. When probabilities are given, securing informed consent generally requires communicating how probability expressions are to be interpreted and communicating something about the quality and quantity of the evidence for (...) the probabilities reported. Patients may also require guidance on how probability claims may or may not be relevant to their decisions, and physicians should be ready to help patients understand these issues. (shrink)

Scientific and medical practice both relate to and differ from each other, as do discussions of how to handle decisions under uncertainty in the laboratory and clinic respectively. While studies of science have pointed out that scientific practice is more complex and messier than dominant conceptions suggest, medical practice has looked to the rigour of scientific and statistical methods to address clinical uncertainty. In this article, we turn to epistemological studies of the laboratory to highlight how clinical practice already has (...) strategies for dealing with messiness. We draw on Hans-Jo ̈rg Rheinberger’s Toward a History of Epistemic Things, in which he invokes the metaphor of a spider’s web to explain the role of tacit practices in experimental biochemistry for helping practitioners manage messiness. We argue that diagnostic practices in clinical medicine employ similar, albeit codified, procedures to evaluate epistemic significance, ensure sensitivity to the unforeseen, and allow focused grounds for action. We consider three practices: (a) the pre-set structure of medical records, ensuring broad coverage in initial anamnesis, (b) the use of lists of differential diagnoses and ongoing ‘anchoring and adjusting’ as inquiry progresses, and (c) shared decision-making as an occasion to synthesize empirical evidence and reopen inquiry for potential missed information. We end by suggesting that while philosophy of medicine may learn from laboratory epistemology, the sciences may learn something from medical practice. (shrink)

Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and (...) ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics. (shrink)

We simulate trial data to test speculative claims about research methods, such as the impact of publication bias.

This article analyses how the French Academy of Sciences assessed Jaime Ferrán’s cholera vaccine submitted for the Prix Bréant in the 1880s. Ferrán, a Spanish independent physician, discovered the treatment in 1884 and tried it on thousands of patients during the cholera outbreak in Valencia the following year. His evaluation sparked a controversy in Spain and abroad on the vaccine’s efficacy. The Bréant jury did not see any evidence for it in Ferrán’s submission, a decision usually interpreted in terms of (...) French scientific nationalism (or simple chauvinism): an outsider from the scientific periphery could not be awarded the Bréant. Drawing on the archival records of the award, we suggest that Ferrán failed instead to provide data that the Academy could consider unbiased, according to the contemporary standards for data presentation. We will illustrate these standards at work in the assessment of another submission from Spain, by Philipp Hauser, who received the Bréant for the thoroughness of his statistical endeavour. (shrink)

Most of the epidemiological models of the Covid-19 pandemic contain the reproduction number as a parameter. In this article we focus on some shortcomings regarding its role in driving health policies and political decisions. First, we summarize what R is and what it is used for. Second, we introduce a three-question matrix for the evaluation of any construct or parameter within a model. We then review the main literature about R to highlight some of its shortcomings and apply to them (...) our three-question matrix. Finally, we argue that these shortcomings are important for an epistemic and political evaluation of R. (shrink)

In the last decades it has become clear that medicine must find some way to combine its scientific and humanistic sides. In other words, an adequate notion of medicine requires an integrative position that mediates between the analytic-reductionist and the normative-holistic tendencies we find therein. This is especially important as these different styles of reasoning separate “illness” (something perceived and managed by the whole individual in concert with their environment) and “disease” (a “mechanical failure” of a biological element within the (...) body). While the demand for an integrative view has typically been motivated by ethical concerns, we claim that it is also motivated, perhaps even more fundamentally, by epistemological and methodological reasons. Evidence-based bio-medicine employs experimental and statistical techniques which eliminate important differences in the ways that conscious humans evaluate, live with, and react to disease and illness. However, it is precisely these experiences that underpin the concepts and norms of bio-medicine. Humanistic disciplines, on the other hand, have the resources to investigate these experiences in an intersubjectively testable way. Medicine, therefore, cannot afford to ignore its nature as a human science; it must be concerned not only with disease and illness, but also with the ways in which patients as persons respond to malady. Insofar as attitudes and expectations influence the criteria of illness and disease, they must be studied as part of the genuine subject matter of medicine as a human science. In general, we urge that this is a necessary step to overcome today's trend to split evidence-based and clinical medicine. (shrink)

To clarify the proper role of values in science, focusing on controversial expert responses to Covid-19, this article examines the status of (in)convenient hypotheses. Polarizing cases like health experts downplaying mask efficacy to save resources for healthcare workers, or scientists dismissing “accidental lab leak” hypotheses in view of potential xenophobia, plausibly involve modifying evidential standards for (in)convenient claims. Societies could accept that scientists handle (in)convenient claims just like nonscientists, and give experts less political power. Or societies could hold scientists to (...) a higher bar, by expecting them not to modify evidential standards to avoid costs only incidentally tied to error. (shrink)

Homoeopathy is commonly recognised as pseudoscience. However, there is, to date, no systematic discussion that seeks to establish this view. In this paper, we try to fill this gap. We explain the nature of homoeopathy, discuss the notion of pseudoscience, and provide illustrative examples from the literature indicating why homoeopathy fits the bill. Our argument contains a conceptual and an empirical part. In the conceptual part, we introduce the premise that a doctrine qualifies as a pseudoscience if, firstly, its proponents (...) claim scientific standing for it and, secondly, if they produce bullshit to defend it, such that, unlike science, it cannot be viewed as the most reliable knowledge on its topic. In the empirical part, we provide evidence that homoeopathy fulfils both criteria. The first is quickly established since homoeopaths often explicitly claim scientificity. To establish the second, we dive into the pseudo-academic literature on homoeopathy to provide evidence of bullshit in the arguments of homoeopaths. Specifically, we show that they make bizarre ontological claims incompatible with natural science, illegitimately shift the burden of proof to sceptics, and mischaracterise, cherry-pick, and misreport the evidence. Furthermore, we demonstrate that they reject essential parts of established scientific methodology and use epistemically unfair strategies to immunise their doctrine against recalcitrant evidence. (shrink)

Evidence-based medicine is a clinical decision making framework which makes claims about what physicians ought to do. Though heralded as the cutting edge of medical science evidence-based medicine is a value laden normative theory which implicitly depends on substantive views regarding what is morally good or right. In this paper, I provide an ethical analysis of evidence-based medicine. I consider its normative underpinnings in three ethical theories: utilitarianism, Kantian deontology, and virtue ethics. In the face of uncertainty, evidence-based medicine endorses (...) expected utility theory using the best available evidence in order to avoid doing more harm than good. In accordance with the Kantian respect for individuals as ends in themselves, evidence-based medicine calls for integrating the values and preferences of the patient. De-emphasizing intuition, clinical expertise, and pathophysiologic rationale emphasizes the need for the intellectual virtues of curiosity, critical thinking, and courage. Evidence-based medicine is a successful clinical practice that can be morally justified by all three major ethical theories. Although its focus on maximizing good health outcomes and integrating respect for individual patients has been emphasized, the role of the intellectual virtues in evidence- based medicine remains highly under-explored. (shrink)

The ethical question is whether university mask mandates should be relaxed. I argue that the use of face masks by healthy individuals has uncertain benefits, which potential harms may outweigh, and should therefore be voluntary. Systematic reviews by the World Health Organization (WHO) and Cochrane Acute Respiratory Infections concluded that the use of face masks by healthy individuals in the community lacks effectiveness in reducing viral transmission based on moderate-quality evidence. The only two randomized controlled trials of face masks published (...) during the pandemic found little to no benefit. Without high-quality evidence, it is difficult to justify a requirement rather than a recommendation. Notwithstanding, one might argue that the precautionary principle justifies mask mandates. If the precautionary principle can justify implementing mask mandates due to the risk of forgoing possible benefit, then it might also be able to justify not implementing mask mandates due to the risk of potential harm caused by the intervention. It is commonly thought that there is little to lose from the use of face masks, but this is not necessarily true. It is possible that masks have done more harm than good. (shrink)

Lockdowns, or modern quarantines, involve the use of novel restrictive non-pharmaceutical interventions (NPIs) to suppress the transmission of COVID-19. In this paper, I aim to critically analyze the emerging history and philosophy of lockdowns, with an emphasis on the communication of health evidence and risk for informing policy decisions. I draw a distinction between evidence-based and modeling-based decision-making. I argue that using the normative framework of evidence-based medicine would have recommended against the use of lockdowns. I first review the World (...) Health Organization’s evidence-based pandemic preparedness plans for respiratory viruses. I then provide a very brief history of COVID-19 modeling, which was cited as justification for the use of lockdowns in the U.K., the U.S., and much of the world. I focus on the so-called Imperial College model designed by Neil Ferguson et al. as well as the so-called Oxford model designed by José Lourenço et al. I analyze the evidence-based pandemic response known as ‘mitigation’, and I compare it with Ferguson et al.’s experimental strategy known as ‘suppression’. I summarize the strengths and weaknesses of these strategies based on their diametric aims and each model’s parametric assumptions. Based on my critical analysis of the suppression strategy, I attempt to expose what has been called the ‘logic of lockdowns’, which Sunetra Gupta of the Oxford model group has suggested is flawed. Finally, I consider Trisha Greenhalgh’s objection to evidence-based policy based on the precautionary principle, and I attempt to offer a response. I conclude with a brief narrative review of the emerging randomized evidence on restrictive NPIs, which seems to support my claim that mitigation was the strategy that would have been recommended by evidence-based medicine. If this is true, then COVID-19 modeling may serve as an important reminder of the enduring lesson of evidence-based medicine: that one should always ‘Trust the Evidence!’ for better health policy. (shrink)

Evidence-based medicine is a clinical decision-making framework which makes claims about what physicians ought to do. Though heralded as the cutting edge of medical science, evidence-based medicine is a value-laden normative theory which implicitly depends on substantive views regarding what is morally good or right. In this paper, I provide an ethical analysis of evidence-based medicine. I consider its normative underpinnings in three ethical theories: utilitarianism, Kantian deontology, and virtue ethics. In the face of uncertainty, evidence-based medicine endorses expected utility (...) theory using the best available evidence in order to avoid doing more harm than good. In accordance with the Kantian respect for individuals as ends in themselves, evidence-based medicine calls for integrating the values and preferences of the patient. De-emphasizing intuition, clinical expertise, and pathophysiologic rationale emphasizes the need for the intellectual virtues of curiosity, critical thinking, and courage. Evidence-based medicine is a successful clinical practice that can be morally justified by all three major ethical theories. Although its focus on maximizing good health outcomes and integrating respect for individual patients has been emphasized, the role of the intellectual virtues in evidence-based medicine remains highly under-explored. (shrink)

For children, the collateral damage of the COVID-19 pandemic response has been considerable. In this paper, we use the framework of evidence-based medicine to argue that child abuse is another negative side effect of COVID-19 lockdowns. While it was certain that school closures would have profound social and economic costs, it remains uncertain whether they have any effect on COVID-19 transmission. There is emerging evidence that lockdowns significantly worsened child abuse on a global scale. Low-income and middle-income countries are particularly (...) vulnerable to increases in child abuse. The best available external evidence from systematic research during the pandemic demonstrates an increase in the risk of child maltreatment, an increase in child maltreatment hospitalisations and a concerning decrease in official child maltreatment referrals. The paradoxical phenomenon of increased hospitalisations and decreased reports is unlikely to be explained by a genuine decrease in child abuse. We conclude that lockdowns have an unacceptably high risk of negative side effects for children, as evidenced by child abuse, the true extent of which appears to be masked by lockdown-related disruptions to schools and other surveillance systems. The desire for a sense of security may be a tempting bias towards emphasising the resilience of children, but it is ethically problematic to push children towards abuse in the name of public health. It is our view that the collateral damage of prolonged school closures for society’s most vulnerable members is a powerful ethical consideration against any pandemic response which involves their use. (shrink)

This paper addresses the recent rise of the use of alternative medicine in Western countries. It offers a novel explanation of that phenomenon in terms of cognitive and economic factors related to the free-market and patient-centric approach to medicine that is currently in place in those countries, in contrast to some alternative explanations of this phenomenon. Moreover, the paper addresses this troubling trend in terms of the serious harms associated with the use of alternative medical modalities. The explanatory theory defended (...) here is then predicated on the idea that an extreme patient-centric model of medical practice that treats largely ignorant patients as consumers of medical products and services endowed with an essentially unrestricted power of freedom to choose treatments predictably leads serious and avoidable harms. Some important moral and epistemological consequences of this model are then articulated and corrective measures are suggested. (shrink)

There are two competing views regarding the role of mechanistic knowledge in inferences about the effectiveness of interventions. One view holds that inferences about the effectiveness of interventions should be based only on data from population-level studies (often statistical evidence from randomised trials). The other view holds that such inferences must be based in part on mechanistic evidence. The competing views are local principles of inference, the plausibility of which can be assessed by a more general normative principle of inference. (...) Bayesianism tells us to base inferences on both the ‘likelihood’ and the ‘prior’. The likelihood represents statistical evidence. One influence on the prior probability of a hypothesis like ‘d causes x’ is mechanistic knowledge of how d causes x. Thus, reasoning about such inferences by appealing to both statistical and mechanistic evidence is vindicated by our best general theory of inference. The primary contribution of this paper is to assess the merits and weaknesses of the arguments on both sides of the debate, using the Bayesian framework. This analysis lends support to those who argue that we should base our causal inferences about interventions in part on mechanistic evidence. (shrink)

Epidemiological explanation often has a “black box” character, meaning the intermediate steps between cause and effect are unknown. Filling in black boxes is thought to improve causal inferences by making them intelligible. I argue that adding information about intermediate causes to a black box explanation is an unreliable guide to pragmatic intelligibility because it may mislead us about the stability of a cause. I diagnose a problem that I call wishful intelligibility, which occurs when scientists misjudge the limitations of certain (...) features of an explanation. Wishful intelligibility gives us a new reason to prefer black box explanations in some contexts. (shrink)

In this paper, we use the case of the COVID-19 pandemic in Europe to address the question of what kind of knowledge we should incorporate into public health policy. We show that policy-making during the COVID-19 pandemic has been biomedicine-centric in that its evidential basis marginalised input from non-biomedical disciplines. We then argue that in particular the social sciences could contribute essential expertise and evidence to public health policy in times of biomedical emergencies and that we should thus strive for (...) a tighter integration of the social sciences in future evidence-based policy-making. This demand faces challenges on different levels, which we identify and discuss as potential inhibitors for a more pluralistic evidential basis. (shrink)

Randomized controlled trials test new drugs using various debiasing devices to prevent participants from manipulating the trials. But participants often dislike controls, arguing that they impose a paternalist constraint on their legitimate preferences. The 21st Century Cures Act, passed by US Congress in 2016, encourages the Food and Drug Administration to use alternative testing methods, incorporating participants’ preferences, for regulatory purposes. We discuss, from a historical perspective, the trade-off between trial impartiality and participants’ freedom. We argue that the only way (...) out is considering which methods improve upon the performance of conventional trials in keeping dangerous or inefficacious compounds out of pharmaceutical markets. (shrink)

Extrapolation of causal claims from study populations to other populations of interest is a problematic issue. The standard approach in experimental research, which prioritises randomized controlled trials and statistical evidence, is not devoid of difficulties. Granted that, it has been defended that evidence of mechanisms is indispensable for causal extrapolation. We argue, contrarily, that this sort of evidence is not indispensable. Nonetheless, we also think that occasionally it may be helpful. In order to clarify its relevance, we introduce a distinction (...) between a positive and a negative role of evidence of mechanisms. Our conclusion is that the former is highly questionable, but the latter may be a trustworthy resource for causal extrapolation. (shrink)

The paper investigates the epistemological and communicative competences the experts need to use and communicate evidence in the reasoning process leading to diagnosis. The diagnosis and diagnosis communication are presented as intertwined processes that should be jointly addressed in medical consultations, to empower patients’ compliance in illness management. The paper presents defeasible reasoning as specific to the diagnostic praxis, showing how this type of reasoning threatens effective diagnosis communication and entails that we should understand diagnostic evidence as defeasible as well. (...) It argues that metaphors might be effective communicative devices to let the patients understand the relevant defeasors in the diagnostic reasoning process, helping to improve effective diagnosis communication, and also encouraging a change in patients’ beliefs and attitudes on their own experience of illness and illness’ management. (shrink)

Evidential Pluralism maintains that in order to establish a causal claim one normally needs to establish the existence of an appropriate conditional correlation and the existence of an appropriate mechanism complex, so when assessing a causal claim one ought to consider both association studies and mechanistic studies. Hitherto, Evidential Pluralism has been applied to medicine, leading to the EBM+ programme, which recommends that evidence-based medicine should systematically evaluate mechanistic studies alongside clinical studies. This paper argues that Evidential Pluralism can also (...) be fruitfully applied to the social sciences. In particular, Evidential Pluralism provides (i) a new approach to evidence-based policy; (ii) an account of the evidential relationships in more theoretical research; and (iii) new philosophical motivation for mixed methods research. The application of Evidential Pluralism to the social sciences is also defended against two objections. (shrink)

The COVID-19 pandemic poses extraordinary public health challenges. In order to respond to such challenges, most democracies have relied on so-called ‘evidence-based’ policies, which supposedly devolve to science the burden of their justification. However, the biomedical sciences can only provide a theory-laden evidential basis, while reliable statistical data for policy support is often scarce. Therefore, scientific evidence alone cannot legitimise COVID-19 public health policies, which are ultimately based on political decisions. Given this inevitable input on policy-making, the risk of arbitrariness (...) is ubiquitous and democratic scrutiny becomes essential to counter it. During the COVID-19 pandemic, the standards of scientific and democratic scrutiny have been, as a matter of fact, substantially lowered. This erosion potentially damages democracy. (shrink)

We live in an age alleged devoted to evidence-based medicine. Evidence-based medicine, however, depends on reliable data and if the data are largely, if not completely, manipulated by the manufacturer of pharmaceuticals, then the data are not reliable. Evidence-based medicine is an illusion. This book raises and attempts to answer the following questions: What are the ways in which the profit motive of industry undermines the integrity of science? How is science protected from corporate malfeasance in a capitalist economy? Our (...) particular focus is the relationship between the pharmaceutical industry and medicine. We argue that medicine desperately needs to re-evaluate its relationship with the pharmaceutical industry and re-affirm the ideal of evidence-based medicine. In our view, Karl Popper’s philosophy of science, Falsificationism and Critical Rationalism, provides a rigorous conception of science required to preserve the integrity of the scientific basis of medicine against the commercial influences. (shrink)

Within the evidence-based medicine construct, clinical expertise is acknowledged to be both derived from primary experience and necessary for optimal medical practice. Primary experience in medical practice, however, remains undervalued. Clinicians’ primary experience tends to be dismissed by EBM as unsystematic or anecdotal, a source of bias rather than knowledge, never serving as the “best” evidence to support a clinical decision. The position that clinical expertise is necessary but that primary experience is untrustworthy in clinical decision-making is epistemically incoherent. Here (...) we argue for the value and utility of knowledge gained from primary experience for the practice of medicine. Primary experience provides knowledge necessary to diagnose, treat, and assess response in individual patients. Hierarchies of evidence, when advanced as guides for clinical decisions, mistake the relationship between propositional and experiential knowledge. We argue that primary experience represents a kind of medical knowledge distinct from the propositional knowledge produced by clinical research, both of which are crucial to determining the best diagnosis and course of action for particular patients. (shrink)

We raise a problem of applicability of RCTs to validate nuclear diagnostic imaging tests. In spite of the wide application of PET and other similar techniques that use radiopharmaceuticals for diagnostic purposes, RCT-based evidence on their validity is sparse. We claim that this is due to a general conceptual problem that we call Prevalence of Treatment, which arises in connection with designing RCTs for testing any diagnostic procedure in the present context of medical research, and is particularly apparent in this (...) case. We also identify three practical reasons why RCTs do not qualify as the best option for PET validation, which have to do with specific characteristics of nuclear diagnostic imaging, and of radiopharmaceuticals. The paper is meant to contribute both to the philosophical discussion on the EBM hierarchy of evidence, and on the specific debate on radiopharmaceuticals in nuclear medicine. (shrink)

Evidence-based medicine (EBM) calls for medical practitioners to “integrate” our best available evidence into clinical practice. A significant amount of the literature on EBM takes this integration to be unproblematic, focusing on questions like how to interpret evidence and engage with patient values, rather than critically looking at how these features of EBM can be implemented together. Other authors have also commented on this gap in the literature, for example, identifying the lack of clarity about how patient preferences and evidence (...) from trials is supposed to be integrated in practice. In this paper, I look at this issue from an epistemological perspective, (looking at how different types of knowledge in EBM can be used to make sounds judgements). In particular, I introduce an epistemological issue for this integration problem, which I call the epistemic integration problem. This is essentially the problem of how we can use information that is both general (eg, about a population sample) and descriptive (eg, about what expected outcomes are) to reach clinical judgements that are individualized (applying to a particular patient) and normative (about what is best for their health). (shrink)

A diagnostic process is an investigative process that takes a clinical picture as input and outputs a diagnosis. We propose a method for distinguishing diagnoses that are warranted from those that are not, based on the cognitive processes of which they are the outputs. Processes designed and vetted to reliably produce correct diagnoses will output what we shall call ‘warranted diagnoses’. The latter are diagnoses that should be trusted even if they later turn out to have been wrong. Our work (...) is based on the recently developed Cognitive Process Ontology and further develops the Ontology of General Medical Science. It also has applications in fields such as intelligence, forensics, and predictive maintenance, all of which rely on vetted processes designed to secure the reliability of their outputs. (shrink)

Over the last decade, philosophers of science have extensively criticized the epistemic superiority of randomized controlled trials for testing safety and effectiveness of new drugs, defending instead various forms of evidential pluralism. We argue that scientific methods in regulatory decision-making cannot be assessed in epistemic terms only: there are costs involved. Drawing on the legal distinction between rules and standards, we show that drug regulation based on evidential pluralism has much higher costs than our current RCT-based system. We analyze these (...) costs and advocate for evaluating any scheme for drug regulatory tests in terms of concrete empirical benchmarks, like the error rates of regulatory decisions. (shrink)

Drug regulation is fraught with inductive risk. Regulators must make a prediction about whether or not an experimental pharmaceutical will be effective and relatively safe when used by typical patients, and such predictions are based on a complex, indeterminate, and incomplete evidential basis. Such inductive risk has important practical consequences. If regulators reject an experimental drug when it in fact has a favourable benefit/harm profile, then a valuable intervention is denied to the public and a company’s material interests are needlessly (...) thwarted. Conversely, if regulators approve an experimental drug when it in fact has an unfavourable benefit/harm profile, then resources are wasted, people are needlessly harmed, and other potentially more effective treatments are underutilized. Given that such regulatory decisions have these practical consequences, non-epistemic values about the relative importance of these consequences impact the way such regulatory decisions are made (similar to the analysis of laboratory studies on the toxic effects of dioxins presented in Douglas [2000]). To balance the competing demands of the pertinent non-epistemic values, regulators must perform what I call an “inductive risk calculus.” At least in the American context this inductive risk calculus is not well-managed. (shrink)

Statistical evidence is pervasive in medicine. In this chapter we will focus on the reliability of randomized clinical trials (RCTs) conducted to test the safety and efficacy of medical treatments. RCTs are scientific experiments and, as such, we expect them to be replicable: if we repeat the same experiment time and again, we should obtain the same outcome (Norton 2015). The statistical design of the test should guarantee that the observed outcome is not a random event, but rather a real (...) effect of the treatments administered. However, for more than a decade now, we have been discussing a replicability crisis across different experimental disciplines including medicine: the outcomes of trials published in very prestigious journals often disappear when the experiment is repeated (see, e.g., Lehrer 2010, Begley and Ellis 2012, Horton 2015). -/- There are different accounts of the reason for this replicability crisis, ranging from scientific fraud to lack of institutional incentives to double-check someone else’s results. In this chapter we will use the replicability crisis as a thread to introduce some central issues in the design of scientific experiments in medicine. First, in section 1, we will see how replicability and statistical significance are connected: we can only make sense of the p-value of a trial outcome within a series of replications of the test. But in order to conduct these replications properly, we need to agree on the proper design of the experiment we are going to repeat. In particular, we need to prevent the preferences of the experimenters from biasing the outcome of the experiment. If there is such a bias, when the experiment is replicated by a third party, the observed outcome will vanish. In section 2, we will argue that trialists need to agree on the debiasing procedures and the statistical quality controls that feature in the trial protocol, if they want the outcome to be replicable. In section 3, we will make two complementary points. On the one hand, replicability per se is not everything: we need trial outcomes that are not only statistically significant but also clinically relevant. On the other hand, trials are not everything: the experts analyzing the evidence can improve the reliability of statistical evidence, although they sometimes fail; we need to further study how they make their decisions. In section 4, we will use a controversy about the overprescription of statins to show how non-replicable effects are obtained in trials and how experts may fail at detecting such flaws, if the commercial interests are big enough. (shrink)

Evidence based medicine (EBM) is under critical debate, and person centered healthcare (PCH) has been proposed as an improvement. But is PCH offered as a supplement or as a replacement of EBM? Prima facie PCH only concerns the practice of medicine, while the contended features of EBM also include methods and medical model. I here argue that there are good philosophical reasons to see PCH as a radical alternative to the existing medical paradigm of EBM, since the two seem committed (...) to conflicting ontologies. This paper aims to make explicit some of the most fundamental assumptions that motivate EBM and PCH, respectively, in order to show that the choice between them ultimately comes down to ontological preference. While EBM has a solid foundation in positivism, or what I here call Humeanism, PCH is more consistent with causal dispositionalism. I conclude that if there is a paradigmatic revolution on the way in medicine, it is first of all one of ontology. (shrink)

Since its inception in the early 1990s, evidence-based medicine (EBM) has become the dominant epistemic framework for Western medical practice. However, in light of powerful criticisms against EBM, alternatives such as casuistic medicine have been gaining support in both the medical and philosophical community. In the absence of empirical evidence in support of the claim that EBM improves patient outcomes, and in light of considerations that it is unlikely that such evidence will be forthcoming, another standard is needed to assess (...) EBM against its alternatives. In this paper, I propose a set of criteria for this purpose based on Helen Longino's criteria for assessing the objectivity of a knowledge productive community. I then apply these criteria to assess EBM against a casuistic framework for medical knowledge. I argue that EBM's strict adherence to a hierarchical organization of knowledge can reasonably be expected to block it from fulfilling a high level of objectivity. A casuistic framework, on the other hand, because it emphasizes critical evaluation in conjunction with the flexibility of a case-based approach, could be expected to better facilitate a more optimal epistemic community. (shrink)

Mechanistic information is used in the field of risk assessment in order to clarify two controversial methodological issues, the selection of inference guides and the definition of standards of evidence. In this paper we present an analysis of the concept of mechanistic information in risk assessment by recurring to previous philosophical analyses of mechanistic explanation. Our conclusion is that the conceptual analysis of mechanistic explanation facilitates a better characterization of the concept of mechanistic information. However, it also shows that the (...) use of this kind of information in risk assessment is heavily influenced by pragmatic factors, which have not been sufficiently taken into account in philosophical analysis. Mechanistic models are like hypothesis that have to be validated empirically. Due to their dependence on the standards of evidence, they are subject to the same pragmatic factors. Therefore, recurring to mechanistic information does not lead to closure of the methodological controversies in risk assessment. (shrink)

This paper is on the problem of causal selection and comments on Collingwood's classic paper "The so-called idea of causation". It discusses the relevance of Collingwood’s control principle in contemporary life sciences and defends that it is not the ability to control, but the willingness to control that often biases us towards some rather than other causes of a phenomenon. Willingness to control is certainly only one principle that influences causal selection, but it is an important one. It shows how (...) norms make causes. (shrink)

Semmelweis’s discovery of the cause of puerperal fever around the middle of the 19th century counts among the paradigm cases of scientific discovery. For several decades, philosophers of science have used the episode to illustrate, appraise and compare views of proper scientific methodology.Here I argue that the episode can be profitably reexamined in light of two cognate notions: causal reasoning and mechanisms. Semmelweis used several causal reasoning strategies both to support his own and to reject competing hypotheses. However, these strategies (...) have gone unappreciated in the existing literature. I show that a causal reasoning approach makes sense of the multitude of tables in Semmelweis’s main text, which in later editions were often abridged because they appeared redundant.Moreover, the existing literature tends to focus on Semmelweis’s clinical intervention and on the extent to which it alone confirms his theoretical conclusions. This neglects Semmelweis’s efforts to show by animal experiments that his clinical results are in agreement with a demonstrable mechanism of puerperal fever pathogenesis. I argue that the full evidential force of Semmelweis’s argument can only be appreciated if both his clinical and his laboratory investigations are taken into account. (shrink)

Did the impartiality of clinical trials play any role in their acceptance as regulatory standards for the safety and efficacy of drugs? According to the standard account of early British trials in the 1930s and 1940s, their impartiality was just rhetorical: the public demanded fair tests and statistical devices such as randomization created an appearance of neutrality. In fact, the design of the experiment was difficult to understand and the British authorities took advantage of it to promote their own particular (...) interests. I claim that this account is based on a poorly defined concept of experimental fairness . I present an alternative approach in which a test would be impartial if it incorporates warrants of non-manipulability. With this concept, I reconstruct the history of British trials showing that they were indeed fair and this fairness played a role in their acceptance as regulatory yardsticks. (shrink)

. This article traces the intellectual history of scientific studies of intercessory prayer published in English between 1965 and the present by focusing on the conflict and discussion they prompted in the medical literature. I analyze these debates with attention to how researchers articulate the possibilities and limits medical science has for studying intercessory prayer over time. I delineate three groups of researchers and commentators: those who think intercessory prayer can and should be studied scientifically, those who are more skeptical (...) and articulate the limits of science around this topic, and those who focus primarily on the pragmatic applications of this knowledge. I analyze these contests as examples of what Thomas Gieryn calls “epistemic authority” as medical researchers engage in what he describes as “boundary‐work” or “the discursive attribution of selected qualities to scientists, scientific methods, and scientific claims for the purposes of drawing a rhetorical boundary between science and some less authoritative residual non‐science.” ). (shrink)

According to Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007, Hist Philos Life Sci 33:389–396, 2011a, Philos Sci 1(1):47–69, 2011b ), in order to establish a causal claim of the form, ‘_C_ is a cause of _E_’, one typically needs evidence that there is an underlying mechanism between _C_ and _E_ as well as evidence that _C_ makes a difference to _E_. This thesis has been used to argue that hierarchies of evidence, as championed by evidence-based movements, tend to (...) give primacy to evidence of difference making over evidence of mechanisms and are flawed because the two sorts of evidence are required and they should be treated on a par. An alternative approach gives primacy to evidence of mechanism over evidence of difference making. In this paper, we argue that this alternative approach is equally flawed, again because both sorts of evidence need to be treated on a par. As an illustration of this parity, we explain how scientists working in the ‘EnviroGenomarkers’ project constantly make use of the two evidential components in a dynamic and intertwined way. We argue that such an interplay is needed not only for causal assessment but also for policy purposes. (shrink)

Evidence Based Medicine is a thesis about reasoning: it champions a certain way of thinking about practice, one based not on the particular, not on thecontext-specific, but on broad statistical concerns. It emphasises “the increasing realisation of the power of probabilistic reasoning” to establish “a new epidemiologic standard”. So the claims of the “EBMers” imply a whole position in medical epistemology. But authors seem unwilling to give a detailed exposition, let alone defence of this position to critics. Hence the accusations (...) of arrogance. (shrink)

Relationships, jobs, and health behaviors-these are what New Year's resolutions are made of. Every year millions resolve to adopt a better diet, exercise more, become fit, or lose weight but few put into practice the health behaviors they aspire to. For those who successfully begin, the likelihood that they will maintain these habits is low. Healthcare professionals recognize the importance of these, and other, health behaviors but struggle to provide their patients with the tools necessary for successful maintenance of their (...) medical regimens. The thousands of research papers that exist on patient adherence and health behavior change can leave professionals overwhelmed. This book synthesizes the results from more than 50 years of empirical research, resulting in simple, powerful, and practical guidance for health professionals who want to know the most effective strategies for helping their clients to put long-term health-relevant behavior changes into practice. It advocates a straightforward 3-ingredient model: Before a person can change, they must know what change is necessary ; desire the change ; and then have the tools to achieve and maintain the change. This book is designed to be informative and compelling, but its numerous anecdotes and examples render it engaging and entertaining, as well. Written for a practitioners and students of medicine, chiropractic, osteopathy, nursing, health education, physician assistant programs, dentistry, clinical and health psychology, marriage and family counseling, social work, school psychology, and care administrators -- and for lay persons who wish to take an active role in their health, this book brings together major empirically-based findings within the field and provides succinct, evidence-based recommendations and strategies for using these findings to make real changes. (shrink)

Drawing on an analysis of Irving Kirsch and colleagues’ controversial 2008 article in PLoS [Public Library of Science] Medicine on the efficacy of SSRI antidepressant drugs such as Prozac, I examine flaws within the methodologies of randomized controlled trials (RCTs) that have made it difficult for regulators, clinicians and patients to determine the therapeutic value of this class of drug. I then argue, drawing analogies to work by Pierre Bourdieu and Michael Power, that it is the very limitations of RCTs (...) — their inadequacies in producing reliable evidence of clinical effects — that help to strengthen assumptions of their superiority as methodological tools. Finally, I suggest that the case of RCTs helps to explore the question of why failure is often useful in consolidating the authority of those who have presided over that failure, and why systems widely recognized to be ineffective tend to assume greater authority at the very moment when people speak of their malfunction. (shrink)