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)

This thesis is a philosophical examination of the explanatory roles of diagnoses in psychiatry. In medicine, diagnoses normally serve as causal explanations of patients’ symptoms. Given that psychiatry is a discipline whose practice is shaped by medical traditions, it is often implied that its diagnoses also serve such explanatory functions. This is evident in clinical texts that portray psychiatric diagnoses as referring to diseases that cause symptoms. However, there are problems which cast doubt on whether such portrayals are justified. I (...) address these problems and examine whether psychiatric diagnoses provide explanations of symptoms. The first problem is conceptual. In diagnostic manuals, psychiatric diagnoses are defined by their symptoms. This suggests that invoking them as explanations of the symptoms amounts to circularity. I argue that this can be resolved with an appropriate conceptual framework that captures the complex semantic values of diagnostic terms and their different uses in clinical discourse. I put forward such a framework based on two-dimensional semantics. The second problem is ontological. Empirical research suggests that diagnostic categories in psychiatry do not correspond to invariant causal types, but are associated with variable combinations of diverse causes that interact across biological, psychological, and social levels. Given this heterogeneity, I argue that psychiatric diagnoses fall short of paradigmatic cases of causal explanation, but that some can still provide other sorts of useful causal explanatory information. The original contribution of this thesis is the illumination of the conceptual relations between diagnoses and symptoms. This philosophical work is important, because it can be brought to valuable application in modifying psychiatric practice. (shrink)

Current debates surrounding the virtues and shortcomings of randomization are symptomatic of a lack of appreciation of the fact that causation can be inferred by two distinct inference methods, each requiring its own, specific experimental design. There is a non-statistical type of inference associated with controlled experiments in basic biomedical research; and a statistical variety associated with randomized controlled trials in clinical research. I argue that the main difference between the two hinges on the satisfaction of the comparability requirement, which (...) is in turn dictated by the nature of the objects of study, namely homogeneous or heterogeneous populations of biological systems. Among other things, this entails that the objection according to which randomized experiments fail to provide better evidence for causation because randomization cannot guarantee comparability is mistaken. (shrink)

Amalgamating evidence from heterogeneous sources and across levels of inquiry is becoming increasingly important in many pure and applied sciences. This special issue provides a forum for researchers from diverse scientific and philosophical perspectives to discuss evidence amalgamation, its methodologies, its history, its pitfalls, and its potential. We situate the contributions therein within six themes from the broad literature on this subject: the variety-of-evidence thesis, the philosophy of meta-analysis, the role of robustness/sensitivity analysis for evidence amalgamation, its bearing on questions (...) of extrapolation and external validity of experiments, its connection with theory development, and its interface with causal inference, especially regarding causal theories of cancer. (shrink)

This paper explores ethical concerns arising in healthcare integration. We argue that integration is necessary imperative for meeting contemporary and future healthcare challenges, a far stronger evidence base for the conditions of its effectiveness is required. In particular, given the increasing emphasis at the policy level for the entire healthcare infrastructure to become better integrated, our analysis of the ethical challenges that follow from the logic of integration itself is timely and important and has hitherto received insufficient attention. We evaluated (...) an educational intervention which aims to improve child health outcomes by making transitions between primary to secondary care more efficient, ensuring children and parents are better supported throughout. The programme provided skills for trainee paediatricians and general practitioners in co-designing integrated clinical services. The key ethical challenges of integrated care that arose from a clinical perspective are: professional identity and autonomy in an integrated working environment; the concomitant extent of professional responsibility in such an environment; and the urgent need for more evidence to be produced on which strategies for integrating at scale can be based. From our analysis we suggest a tentative way forward, viewed from a normative position broadly situated at the intersection of deontology and care ethics. We adopt this position because the primary clinical ethical issues in the context of integrated care concern: how to ensure that all duties of care to individual patients are met in a newly orientated working environment where clinical responsibility may be ambiguous; and the need to orientate care around the patient by foregrounding their autonomous preferences and ensuring good patient clinician relationships in clinical decision-making. (shrink)

Experimental design is one aspect of a scientific method. A well-designed, properly conducted experiment aims to control variables in order to isolate and manipulate causal effects and thereby maximize internal validity, support causal inferences, and guarantee reliable results. Traditionally employed in the natural sciences, experimental design has become an important part of research in the social and behavioral sciences. Experimental methods are also endorsed as the most reliable guides to policy effectiveness. Through a discussion of some of the central concepts (...) associated with experimental design, including controlled variation and randomization, this chapter will provide a summary of key ethical issues that tend to arise in experimental contexts. In addition, by exploring assumptions about the nature of causation and by analyzing features of causal relationships, systems, and inferences in social contexts, this chapter will summarize the ways in which experimental design can undermine the integrity of not only social and behavioral research but policies implemented on the basis of such research. (shrink)

Cancer research is experiencing ‘paradigm instability’, since there are two rival theories of carcinogenesis which confront themselves, namely the somatic mutation theory and the tissue organization field theory. Despite this theoretical uncertainty, a huge quantity of data is available thanks to the improvement of genome sequencing techniques. Some authors think that the development of new statistical tools will be able to overcome the lack of a shared theoretical perspective on cancer by amalgamating as many data as possible. We think instead (...) that a deeper understanding of cancer can be achieved by means of more theoretical work, rather than by merely accumulating more data. To support our thesis, we introduce the analytic view of theory development, which rests on the concept of plausibility, and make clear in what sense plausibility and probability are distinct concepts. Then, the concept of plausibility is used to point out the ineliminable role played by the epistemic subject in the development of statistical tools and in the process of theory assessment. We then move to address a central issue in cancer research, namely the relevance of computational tools developed by bioinformaticists to detect driver mutations in the debate between the two main rival theories of carcinogenesis. Finally, we briefly extend our considerations on the role that plausibility plays in evidence amalgamation from cancer research to the more general issue of the divergences between frequentists and Bayesians in the philosophy of medicine and statistics. We argue that taking into account plausibility-based considerations can lead to clarify some epistemological shortcomings that afflict both these perspectives. (shrink)

The standardised randomised clinical trial (RCT) has been exceedingly popular in medical research, economics, and practical policy making. Recently, RCTs have faced criticism. First, it has been argued by John Worrall that we cannot be certain that our sample is not atypical with regard to possible confounding factors. I will argue that at least in the case of medical research, we know enough about the relevant causal mechanisms to be justified to ignore a number of factors we have good reason (...) not to expect to be disruptive. I will also argue against an argument provided by Nancy Cartwright and Eileen Munro that RCTs should not be taken to deductively infer probabilistic causal claims, but ampliatively. The paper will end on a discussion of evidence hierarchies and a defence of the stance of evidence-based medicine that RCTs are the best available method to assess a treatment’s efficacy. (shrink)

Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive (...) rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. We here develop such guidelines by first giving a philosophical analysis of the underpinnings of Hill’s viewpoints on causality. We then put forward an evidence-amalgamation framework adopting a Bayesian net approach to model causal inference in pharmacology for the assessment of harms. Our framework accommodates a number of intuitions already expressed in the literature concerning the EBM vs. pluralist debate on causal inference, evidence hierarchies, causal holism, relevance, and reliability. (shrink)

The disconnect problem arises wherever there is ongoing and severe discordance between the scientific assessment of a politically relevant issue, and the politics and legislation of said issue. Here, we focus on the disconnect problem as it arises in the case of climate change, diagnosing a failure to respect the necessary tradeoff between authority and autonomy within a public institution like science. After assessing the problematic deployment of scientific authority in this arena, we offer suggestions for how to mitigate climate (...) change’s particular disconnect problem, as well as more general proposals for reforming science advising. (shrink)

Even though the evidence‐based medicine movement (EBM) labels mechanisms a low quality form of evidence, consideration of the mechanisms on which medicine relies, and the distinct roles that mechanisms might play in clinical practice, offers a number of insights into EBM itself. In this paper, I examine the connections between EBM and mechanisms from several angles. I diagnose what went wrong in two examples where mechanistic reasoning failed to generate accurate predictions for how a dysfunctional mechanism would respond to intervention. (...) I then use these examples to explain why we should expect this kind of mechanistic reasoning to fail in systematic ways, by situating these failures in terms of evolved complexity of the causal system(s) in question. I argue that there is still a different role in which mechanisms continue to figure as evidence in EBM: namely, in guiding the application of population‐level recommendations to individual patients. Thus, even though the evidence‐based movement rejects one role in which mechanistic reasoning serves as evidence, there are other evidentiary roles for mechanistic reasoning. This renders plausible the claims of some critics of evidencebased medicine who point to the ineliminable role of clinical experience. Clearly specifying the ways in which mechanisms and mechanistic reasoning can be involved in clinical practice frames the discussion about EBM and clinical experience in more fruitful terms. (shrink)

Philosophers of science have insisted that evidence of underlying mechanisms is required to support claims about the effects of medical interventions. Yet evidence about mechanisms does not feature on dominant evidence-based medicine “hierarchies.” After arguing that only inferences from mechanisms (“mechanistic reasoning”)—not mechanisms themselves—count as evidence, I argue for a middle ground. Mechanistic reasoning is not required to establish causation when we have high-quality controlled studies; moreover, mechanistic reasoning is more problematic than has been assumed. Yet where the problems can (...) be overcome, mechanistic reasoning can and should be used as evidence. (shrink)

Robin Nunn has argued that we should stop using the terms ‘placebo’ and ‘placebo effect’. I argue in support of Nunn’s position by considering the logic of why we perform placebo comparisons. Like all comparisons, placebo comparison is just a case of comparing one thing with another, but it is a mistake, I argue, to think of placebo comparison as a case where something is compared to ‘a placebo’. Rather, placebo comparison should be understood as a situation which sets-up the (...) treatment and control groups in a particular way; not as a case involving objects or procedures called ‘placebos’ employed in order to control for ‘placebo effects’. (shrink)

Evidence-based policy is gaining support in many areas of government and in public affairs more generally. In this paper we outline what evidence—based policy is then discuss its strengths and weaknesses. In particular, we argue that it faces a serious challenge to provide a plausible account of evidence. This account needs to be at least in the spirit of the hierarchy of evidence subscribed to by evidence-based medicine (from which evidence—based policy derives its name and inspiration). Yet evidence-based policy’s hierarchy (...) needs to be tailored to the kinds of evidence relevant and available to the policy arena. The evidence required for policy decisions does not easily lend itself to randomised controlled trials (the "gold standard" in evidence-based medicine), nor, for that matter, being listed in a single all—purpose hierarchy. (shrink)

A resilient issue in research ethics is whether and when a placebo-controlled trial is justified if it deprives research subjects of a recognized treatment. The clinicians' moral duty to provide the best available care seems to require the use of ‘active’ controlled trials that use an established treatment as a control whenever such a therapy is available. In another regard, ACTs are supposedly methodologically inferior to PCTs. Hence, the moral duty of the clinical researcher to use the best methods will (...) favor PCTs. In this target article, I analyze the three reasons for believing that ACTs are inferior to PCTs namely: 1) ACTs lack ‘assay sensitivity’; 2) ACTs do not measure absolute effect size; and 3) ACTs require more participants; and I contend that none are acceptable. Consequently the tension between clinical and research ethics dissolves: the moral duty of the clinician to avoid PCTs is unopposed by methodological considerations. (shrink)

Proponents of evidence-based medicine (EBM) provide the “hierarchy of evidence” as a criterion for judging the reliability of therapeutic decisions. EBM's hierarchy places randomized interventional studies (and systematic reviews of such studies) higher in the hierarchy than observational studies, unsystematic clinical experience, and basic science. Recent philosophical work has questioned whether EBM's special emphasis on evidence from randomized interventional studies can be justified. Following the critical literature, and in particular the work of John Worrall, I agree that many of the (...) arguments put forward by advocates of EBM do not justify the ambitious claims that are often made on behalf of randomization. However, in contrast to the recent philosophical work, I argue that a justification for EBM's hierarchy of evidence can be provided. The hierarchy should be viewed as a hierarchy of comparative internal validity. Although this justification is defensible, the claims that EBM's hierarchy substantiates when viewed in this way are considerably more circumscribed than some claims found in the EBM literature. (shrink)

Obviously medicine should be evidence-based. The issues lie in the details: what exactly counts as evidence? Do certain kinds of evidence carry more weight than others? (And if so why?) And how exactly should medicine be based on evidence? When it comes to these details, the evidence-based medicine (EBM) movement has got itself into a mess – or so it will be argued. In order to start to resolve this mess, we need to go 'back to basics'; and that means (...) turning to the philosophy of science. The theory of evidence, or rather the logic of the interrelations between theory and evidence, has always been central to the philosophy of science – sometimes under the alias of the 'theory of confirmation'. When taken together with a little philosophical commonsense, this logic can help us move towards a position on evidence in medicine that is more sophisticated and defensible than anything that EBM has been able so far to supply. (shrink)

Evidence-Based Medicine has little consideration for mechanisms and philosophers of science and medicine have recently made pleas to increase the place of mechanisms in the medical evidence hierarchy. However, in this debate the notions of mechanisms seem to be limited to 'mechanistic processes' and 'complex-systems mechanisms,' understood as 'componential causal systems'. I believe that this will not do full justice to how mechanisms are used in biological, psychological and social sciences and, consequently, in a more biopsychosocial approach to medicine. Here, (...) I propose, following (Kuorikoski, 2009), to pay more attention to 'abstract forms of interaction' mechanisms. The present work scrutinized review articles on depression and medically unexplained pain, which are considered to be of multifactorial pathogenesis, for their use of mechanisms. In review articles on these disorders there seemed to be a range of uses between more 'abstract forms of interaction' and 'componential causal system' mechanisms. I therefore propose to expand the notions of mechanisms considered in medicine to include that of more 'abstract forms of interaction' to better explain and manage biopsychosocial disorders. (shrink)

The article addresses the question of how should scientific peers revise their beliefs (if at all) upon recognized disagreement. After presenting the basics of peer disagreement in sections 1 and 2, we focus, in section 3, on a concrete case of scientific disagreement, to wit, the dispute over the evidential status of randomized control trials in medical practice. The examination of this case motivates the idea that some scientific disagreements permit a steadfast reaction. In section 4, we support this conclusion (...) by providing a normative argument in the same direction; if we are correct, typical reasons for conciliation are absent in this kind of scientific disagreements. (shrink)

There are some philosophical questions that can be answered without attention to the social context in which evidence is produced and distributed.ing away from social context is an excellent way to ignore messy details and lay bare the underlying structure of the limits of inference. Idealization is entirely appropriate when one is essentially asking: In the best of all possible worlds, what am I entitled to infer? Yet, philosophers’ concerns often go beyond this domain. As an example I examine the (...) debate on mechanistic evidence and then reevaluate a canonical case study in this debate. I show that for the assessment of actual evidence, produced in a world that is far from ideal, omission of the social aspects of medical epistemology leads philosophers to draw the wrong lessons from cases they take as paradigmatic cases for their views. I close by arguing that social epistemology provides an avenue to incorporate these complications and provides the necessary framework to understand medical evidence. (shrink)

Arguments in favor of greater research-practice integration in medicine have tended to be ethical, political, or pragmatic. There are good epistemic reasons to pursue greater integration, and it is important to think through these reasons in order to avoid inadvertently designing new systems in ways that replicate the epistemic elitism common within current systems. Meaningful transformation within health care is possible with close attention to all reasons in favor of greater research-practice integration, including epistemic reasons.

This paper looks at an appeal to the authority of biomedical research that has recently been used by empirical economists to motivate and justify their methods. I argue that those who make this appeal mistake the nature of biomedical research. Randomised trials, which are said to have revolutionised biomedical research, are a central methodology, but according to only one paradigm. There is another paradigm at work in biomedical research, the inferentialist paradigm, in which randomised trials play no special role. I (...) outline the inferentialist alternative in broad strokes, apply it to a recent controversy in econometrics and draw some general conclusions concerning econometric methodology. (shrink)

Proponents of evidence-based medicine have argued convincingly for applying this scientific method to medicine. However, the current methodological framework of the EBM movement has recently been called into question, especially in epidemiology and the philosophy of science. The debate has focused on whether the methodology of randomized controlled trials provides the best evidence available. This paper attempts to shift the focus of the debate by arguing that clinical reasoning involves a patchwork of evidential approaches and that the emphasis on evidence (...) hierarchies of methodology fails to lend credence to the common practice of corroboration in medicine. I argue that the strength of evidence lies in the evidence itself, and not the methodology used to obtain that evidence. Ultimately, when it comes to evaluating the effectiveness of medical interventions, it is the evidence obtained from the methodology rather than the methodology that should establish the strength of the evidence. (shrink)

The problem of collecting, analyzing and evaluating evidence on adverse drug reactions (ADRs) is an example of the more general class of epistemological problems related to scientific inference and prediction, as well as a central problem of the health-care practice. Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with a special (...) attention to the privileged role assigned to randomized controlled trials in Evidence Based Medicine. Regardless of whether the RCT’s privilege holds for efficacy assessment, it is nevertheless important to make a distinction between causal inference of intended and unintended effects, in that the unknowns at stake are heterogonous in the two contexts. This point has been emphasized by epidemiologists in the last decade. Their main focus is methodological, and regards the fact that bias and confounding do not affect studies on intended and unintended effects in the same way. However, deeper concerns ground the intuition for such a distinction; these are related to the constraints which we impose on evidence and their epistemological justification. My thesis is that such constraints ought to be understood to be different in the case of evidence for risk vs. benefit assessment. I present the recent debate on the causal association between acetaminophen and asthma in order to illustrate the point at issue. (shrink)

Evidence-based medicine (EBM) puts forward a hierarchy of evidence for informing therapeutic decisions. An unambiguous interpretation of how to apply EBM's hierarchy has not been provided in the clinical literature. However, as much as an interpretation is provided proponents suggest a categorical interpretation. The categorical interpretation holds that all the results of randomised trials always trump evidence from lower down the hierarchy when it comes to informing therapeutic decisions. Most of the critical replies to EBM react to this interpretation. While (...) proponents of EBM can avoid some of the problems raised by critics by suitably limited the claims made on behalf of the hierarchy, further problems arise. If EBM is to inform therapeutic decisions then a considerably more restricted, and context dependent interpretation of EBM's hierarchy is needed. (shrink)

Developments in Machine Learning (ML) have attracted attention in a wide range of healthcare fields to improve medical practice and the benefit of patients. Particularly, this should be achieved by providing more or less automated decision recommendations to the treating physician. However, some hopes placed in ML for healthcare seem to be disappointed, at least in part, by a lack of transparency or traceability. Skepticism exists primarily in the fact that the physician, as the person responsible for diagnosis, therapy, and (...) care, has no or insufficient insight into how such recommendations are reached. The following paper aims to make understandable the specificity of the deliberative model of a physician-patient relationship that has been achieved over decades. By outlining the (social-)epistemic and inherently normative relationship between physicians and patients, I want to show how this relationship might be altered by non-traceable ML recommendations. With respect to some healthcare decisions, such changes in deliberative practice may create normatively far-reaching challenges. Therefore, in the future, a differentiation of decision-making situations in healthcare with respect to the necessary depth of insight into the process of outcome generation seems essential. (shrink)

Varied evidence for a hypothesis confirms it more strongly than less varied evidence, ceteris paribus. This epistemological Variety of Evidence Thesis enjoys long-standing widespread intuitive support. Recent literature has raised serious doubts that the correlational approach of explicating the thesis can vindicate it. By contrast, the eliminative approach due to Horwich vindicates the Variety of Evidence Thesis but only within a relatively narrow domain. I investigate the prospects of extending the eliminative approach to a larger domain by considering a larger (...) class of sensible explications of evidential variety. For a large subclass class of such explications I show how to construct cases in which the less diverse body of evidence for a hypothesis confirms more strongly. I hence argue that these prospects are dire since the eliminative approach widely fails to vindicate the thesis. (shrink)

Evidence-based policy has support in many areas of government and in public affairs more generally. In this paper we outline what evidence-based policy is, then we discuss its strengths and weaknesses. In particular, we argue that it faces a serious challenge to provide a plausible, over-arching account of evidence. We contrast evidence-based policy with evidence-based medicine, especially the role of evidence in assessing the effectiveness of medicines. The evidence required for policy decisions does not easily lend itself to randomized controlled (...) trials, nor, for that matter, being listed in a single all-purpose hierarchy. (shrink)

Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with a special attention to the privileged role assigned to randomized controlled trials (RCTs) in evidence based medicine. Regardless of whether the RCT’s privilege holds for efficacy assessment, it is nevertheless important to make a distinction between causal inference of intended and unintended (...) effects, in that the unknowns at stake are heterogonous in the two contexts. However, although “lower level” evidence is increasingly acknowledged to be a valid source of information contributory to assessing the risk profile of medications on theoretical or empirical grounds, current practices have difficulty in assigning a precise epistemic status to this kind of evidence because they are more or less implicitly parasitic on the (statistical) methods developed to test drug efficacy. My thesis is that (1) “lower level” evidence is justified on distinct grounds and at different conditions depending on the different epistemologies which one wishes to endorse, in that each impose different constraints on the methods we adopt to collect and evaluate evidence; (2) such constraints ought to be understood to be different in the case of evidence for risk versus benefit assessment for a series of reasons which I will illustrate on the basis of the recent debate on the causal association between acetaminophen (a.k.a. paracetamol) and asthma. (shrink)

The problem of collecting, analyzing and evaluating evidence on adverse drug reactions (ADRs) is an example of the more general class of epistemological problems related to scientific inference and prediction, as well as a central problem of the health-care practice. Philosophical discussions have critically analysed the methodological pitfalls and epistemological implications of evidence assessment in medicine, however they have mainly focused on evidence of treatment efficacy. Most of this work is devoted to statistical methods of causal inference with a special (...) attention to the privileged role assigned to randomized controlled trials in Evidence Based Medicine. Regardless of whether the RCT’s privilege holds for efficacy assessment, it is nevertheless important to make a distinction between causal inference of intended and unintended effects, in that the unknowns at stake are heterogonous in the two contexts. This point has been emphasized by epidemiologists in the last decade. Their main focus is methodological, and regards the fact that bias and confounding do not affect studies on intended and unintended effects in the same way. However, deeper concerns ground the intuition for such a distinction; these are related to the constraints which we impose on evidence and their epistemological justification. My thesis is that such constraints ought to be understood to be different in the case of evidence for risk vs. benefit assessment. I present the recent debate on the causal association between acetaminophen and asthma in order to illustrate the point at issue. (shrink)

Evidence-Based Medicine (EBM) developed from the work of clinical epidemiologists at McMaster University and Oxford University in the 1970s and 1980s and self-consciously presented itself as a "new paradigm" called "evidence-based medicine" in the early 1990s. The techniques of the randomized controlled trial, systematic review and meta-analysis have produced an extensive and powerful body of research. They have also generated a critical literature that raises general concerns about its methods. This paper is a systematic review of the critical literature. It (...) finds the description of EBM as a Kuhnian paradigm helpful and worth taking further. Three kinds of criticism are evaluated in detail: criticisms of procedural aspects of EBM (especially from Cartwright, Worrall and Howick), data showing the greater than expected fallibility of EBM (Ioaanidis and others), and concerns that EBM is incomplete as a philosophy of science (Ashcroft and others). The paper recommends a more instrumental or pragmatic approach to EBM, in which any ranking of evidence is done by reference to the actual, rather than the theoretically expected, reliability of results. Emphasis on EBM has eclipsed other necessary research methods in medicine. With the recent emphasis on translational medicine, we are seeing a restoration of the recognition that clinical research requires an engagement with basic theory (e.g. physiological, genetic, biochemical) and a range of empirical techniques such as bedside observation, laboratory and animal studies. EBM works best when used in this context. (shrink)

In the biomedical, behavioural and social sciences, the leading method used to estimate causal effects is commonly randomised controlled trials (RCTs) that are generally viewed as both the source and justification of the most valid evidence. In studying the foundation and theory behind RCTs, the existing literature analyses important single issues and biases in isolation that influence causal outcomes in trials (such as randomisation, statistical probabilities and placebos). The common account of biased causal inference is described in a general way (...) in terms of probabilistic imbalances between trial groups. This paper expands the common account of causal bias by distinguishing between the range of biases arising between trial groups but also within one of the groups or across the entire sample during trial design, implementation and analysis. This is done by providing concrete examples from highly influential RCT studies. In going beyond the existing RCT literature, the paper provides a broader, practice-based account of causal bias that specifies the between-group, within-group and across-group biases that affect the estimated causal results of trials – impacting both the effect size and statistical significance. Within this expanded framework, we can better identify the range of different types of biases we face in practice and address the central question about the overall validity of the RCT method and its causal claims. A study can face several smaller biases (related simultaneously to a smaller sample, smaller estimated effect, greater unblinding etc.) that generally add up to greater aggregate bias. Though difficult to measure precisely, it is important to assess and provide information in studies on how much different sources of bias, combined, can explain the estimated causal effect. The RCT method is thereby often the best we have to inform our policy decisions – and the evidence is strengthened when combined with multiple studies and other methods. Yet there is room for continually improving trials and identifying ways to reduce biases they face and to increase their overall validity. Implications are discussed. (shrink)

The intuitive Variety of Evidence Thesis states that, ceteris paribus, more varied evidence for a hypothesis confirms it more strongly than less varied evidence. Recent Bayesian analyses have raised serious doubts in its validity. Claveau suggests the existence of a novel type of counter-example to this thesis: a gradual increase in source independence can lead to a decrease in hypothesis confirmation. I show that Claveau’s measure of gradual source independence suffers from two unsuspected types of inconsistencies. I hence put forward (...) a more natural measure of gradual source independence which is not plagued by inconsistencies. Claveau’s counter-examples to the variety of evidence thesis disappear with the measure I suggest. I hence argue that my measure is preferable and that this thesis does at least not seem to be troubled by Claveau’s arguments. (shrink)

Over the past seven decades, randomised controlled trials (RCTs) have revolutionised clinical research and achieved a gold standard status. However, extending their use to evaluate complex interventions is problematic. In this paper we will demonstrate that complex intervention RCTs violate the necessary premises that govern the RCTs logic and underpin their rigour. The lack of blinding, heterogeneity of participants, as well as poor treatment standardisation and difficulty of controlling for confounders, which characterise complex intervention RCTs, can potentially be profoundly detrimental (...) to their integrity. Proponents of this approach argue that matching “real world” circumstances, while maintaining the randomised design, enhances external validity. We counter this argument by pointing out that an inverted U relation exists between internal and external validity, and thus relaxing the experimental conditions beyond a certain threshold can potentially paradoxically render the RCT externally invalid, i.e. its results cannot be used anywhere. We shall illustrate the inappropriate use of RCTs to evaluate community treatment orders and propose an alternative epistemic model that is based on mechanistic reasoning and Cartwright's capacity concept. (shrink)

This article considers the prospects of inference to the best explanation as a method of confirming causal claims vis-à-vis the medical evidence of mechanisms. I show that IBE is actually descriptive of how scientists reason when choosing among hypotheses, that it is amenable to the balance/weight distinction, a pivotal pair of concepts in the philosophy of evidence, and that it can do justice to interesting features of the interplay between mechanistic and population level assessments.

Hierarchies of evidence are an important and influential tool for appraising evidence in medicine. In recent years, hierarchies have been formally adopted by organizations including the Cochrane Collaboration [1], NICE [2,3], the WHO [4], the US Preventive Services Task Force [5], and the Australian NHMRC [6,7]. The development of such hierarchies has been regarded as a central part of Evidence-Based Medicine, a movement within healthcare which prioritises the use of epidemiological evidence such as that provided by Randomised Controlled Trials. Philosophical (...) work on the methodology of medicine has so far mostly focused on claims about the superiority of RCTs, and hence has largely neglected the questions of what hierarchies are, what assumptions they require, and how they affect clinical practice. This thesis shows that there is great variation in the hierarchies defended and in the interpretations they are, and can be, given. The interpretative assumptions made in using hierarchies are crucial to the content and defensibility of the underlying philosophical commitments concerning evidence and medical practice. Once this variation is been identified, it becomes clear that the little philosophical work that has been done so far affects only some hierarchies, under some interpretations. Modest interpretations offered by La Caze [11], conditional hierarchies like GRADE [12-14], and heuristic approaches such as that defended by Howick et al. [15,16] all survive previous philosophical criticism. This thesis extends previous criticisms by arguing that modest interpretations are so weak as to be unhelpful for clinical practice; that GRADE and similar conditional models omit clinically relevant information, such as information about variation in treatments’ effects and the causes of different responses to therapy; and that heuristic approaches lack the necessary empirical support. The conclusion is that hierarchies in general embed untenable philosophical assumptions: principally that information about average treatment effects backed by high-quality evidence can justify strong recommendations, and that the impact of evidence from individual studies can and should be appraised in isolation. Hierarchies are a poor basis for the application of evidence in clinical practice. The Evidence-Based Medicine movement should move beyond them and explore alternative tools for appraising the overall evidence for therapeutic claims. (shrink)

This paper considers a key point of contention between classical and Bayesian statistics that is brought to the fore when examining so-called ‘persistent experimenters’—the issue of stopping rules, or more accurately, outcome spaces, and their influence on statistical analysis. First, a working definition of classical and Bayesian statistical tests is given, which makes clear that (1) once an experimental outcome is recorded, other possible outcomes matter only for classical inference, and (2) full outcome spaces are nevertheless relevant to both the (...) classical and Bayesian approaches, when it comes to planning/choosing a test. The latter point is shown to have important repercussions. Here we argue that it undermines what Bayesians may admit to be a compelling argument against their approach—the Bayesian indifference to persistent experimenters and their optional stopping rules. We acknowledge the prima facie appeal of the pro-classical ‘optional stopping intuition’, even for those who ordinarily have Bayesian sympathies. The final section of the paper, however, provides three error theories that may assist a Bayesian in explaining away the apparent anomaly in their reasoning. (shrink)

Despite the consensus promoted by the evidence-based medicine framework, many authors continue to express doubts about the superiority of randomized controlled trials. This paper evaluates four objections targeting the legitimacy, feasibility, and extrapolation problems linked to the experimental practice of random allocation. I argue that random allocation is a methodologically sound and feasible practice contributing to the internal validity of controlled experiments dealing with heterogeneous populations. I emphasize, however, that random allocation is solely designed to ensure the validity of causal (...) inferences at the level of groups. By itself, random allocation cannot enhance test precision, doesn’t contribute to external validity, and limits the applicability of causal claims to individuals. (shrink)

The initial successes in recent years in harnessing machine learning technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as (...) the agent responsible for the implementation of diagnosis, therapy, and care is unable to access the generation of findings and recommendations. There is widespread agreement that, generally, a complete traceability is preferable to opaque recommendations; however, there are differences about addressing ML-based systems whose functioning seems to remain opaque to some degree—even if so-called explicable or interpretable systems gain increasing amounts of interest. This essay approaches the epistemic foundations of ML-generated information specifically and medical knowledge generally to advocate differentiations of decision-making situations in clinical contexts regarding their necessary depth of insight into the process of information generation. Empirically accurate or reliable outcomes are sufficient for some decision situations in healthcare, whereas other clinical decisions require extensive insight into ML-generated outcomes because of their inherently normative implications. (shrink)

At least historically, it was common for medical practitioners to believe causal hypotheses on the basis of standalone mechanistic reasoning. However, it is now widely acknowledged that standalone mechanistic reasoning is insufficient for appropriately believing a causal hypothesis in medicine, thanks in part to the so-called problem of masking. But standalone mechanistic reasoning is not the only type of mechanistic reasoning. When exactly then is it appropriate to believe a causal hypothesis on the basis of mechanistic reasoning? In this paper, (...) I argue that it has proved difficult to provide a satisfying answer to this question. I also argue that this difficulty is predicted by recent work in knowledge-first epistemology. I think this shows that recent work in epistemology has important implications for practice in the philosophy of science. It is therefore worth paying closer attention in the philosophy of science to this recent work in knowledge-first epistemology. (shrink)

Proponents of evidence-based medicine and some philosophers of science seem to agree that knowledge of mechanisms can help solve the problem of applying results of controlled studies to target populations (‘the problem of extrapolation’). We describe the problem of extrapolation, characterize mechanisms, and outline how mechanistic knowledge might be used to solve the problem. Our main thesis is that there are four often overlooked problems with using mechanistic knowledge to solve the problem of extrapolation. First, our understanding of mechanisms is (...) often (and arguably, likely to remain) incomplete. Secondly, knowledge of mechanisms is not always applicable outside the tightly controlled laboratory conditions in which it is gained. Thirdly, mechanisms can behave paradoxically. Fourthly, as Daniel Steel points out, using mechanistic knowledge faces the problem of the ‘extrapolator’s circle’. At the same time, when the problems with mechanistic knowledge have been addressed, such knowledge can and should be used to mitigate (nothing can entirely solve) the problem of extrapolation. (shrink)

This paper discusses the outstanding problem of replicability of empirical data in the context of recent work on meta-analysis, especially within the field of evidence-based medicine. Specifically, it deals with the methodological issue of how to determine the degrees of heterogeneity between different collected studies. After critically reviewing the standard measures used to quantify meta-analytical heterogeneity, we argue that they should be revised in such a way to take into account the statistical power of the individual studies. We thus propose (...) some new measures of heterogeneity. Subsequently, we apply them to re-assess concrete case-studies from clinical research, thereby showing explicitly how the relevant values of heterogeneity diverge from those obtained with the original measures. (shrink)

Philosophical theories of evidence have been on offer, but they are mostly evaluated in terms of all-or-none desiderata — if they fail to meet one of the desiderata, they are not a satisfactory theory. In this thesis, I aim to accomplish three missions. Firstly, I construct a new way of evaluating theories of evidence, which I call a tool-based view. Secondly, I analyse the nature of what I will call the various relevance-mediating vehicles that each theory of evidence employs. Thirdly, (...) I articulate the comparative core of evidential reasoning in the historical sciences, one which is overlooked in major theories of evidence. On the first mission, I endorse a meta-thesis of pluralism on theories of evidence, namely a tool-based view. I regard a theory of evidence as a purpose-specific and setting-sensitive tool which has its own strengths, difficulties and limitations. Among the major theories of evidence I have reviewed, I focus on Achinstein’s explanationist theory, Cartwright’s argument theory and Reiss’s inferentialist account, scrutinising and evaluating them against the purposes they set out and the scope of their applications. On the second mission, I note that there is no such thing as intrinsically ‘being evidence’. Rather, I hold that relevance-mediating vehicles configure data, materials or claims in such ways that some of them are labelled evidence. I identify the relevance-mediating vehicles that the theories of evidence employ. On the final mission, I argue that the likelihoodist account is an appropriate tool for explaining the evidential reasoning in poorly specified settings where likelihoods can be only imprecisely compared. Such settings, I believe, are typical in the historical sciences. Using the reconstruction of proto-sounds in historical linguistics as a case study, I formalise the rationale behind it by means of the law of likelihood. (shrink)