The explanatory/pragmatic-trial distinction enjoys a burgeoning philosophical and medical literature and a significant contingent of support among philosophers and healthcare stakeholders as an important way to assess the design and results of randomized controlled trials. A major motivation has been the need to provide relevant, generalizable data to drive healthcare decisions. While talk of pragmatic and explanatory trials could be seen as convenient shorthand, the distinction can also be seen as harboring deeper issues related to inferential strategies used to evaluate (...) causal claims regarding medical treatments. A comprehensive, critical analysis of the distinction and underlying epistemological framework upon which the distinction is based, particularly with respect to treatment effectiveness, has yet to be forthcoming. I provide this, analyzing the distinction’s relationship to generalizability and cognate distinctions between ideal conditions and real-world practice, internal and external validity, and efficacy and effectiveness. I also analyze recent philosophical work that relies on the explanatory/pragmatic-trial distinction and that advocates for more pragmatic trials. I conclude that as an organizing principle for trial-design decisions and trial evaluation, the explanatory/pragmatic-trial distinction is conceptually problematic and not as useful as its proponents seem to think. Since some pragmatic-trial features can be inimical to establishing treatment effectiveness, pragmatic-trial features should not be conflated with pragmatic trials’ avowed goals. If the distinction is to be useful, it and some associated concepts, including generalizability, should be reformulated, lest they continue to underlie a medical epistemology that could contribute to methodologically flawed and potentially unethical advice for the design and interpretation of trials. (shrink)

Measuring the effectiveness of medical interventions faces three epistemological challenges: the choice of good measuring instruments, the use of appropriate analytic measures, and the use of a reliable method of extrapolating measures from an experimental context to a more general context. In practice each of these challenges contributes to overestimating the effectiveness of medical interventions. These challenges suggest the need for corrective normative principles. The instruments employed in clinical research should measure patient-relevant and disease-specific parameters, and should not be sensitive (...) to parameters that are only indirectly relevant. Effectiveness always should be measured and reported in absolute terms (using measures such as 'absolute risk reduction'), and only sometimes should effectiveness also be measured and reported in relative terms (using measures such as 'relative risk reduction')-employment of relative measures promotes an informal fallacy akin to the base-rate fallacy, which can be exploited to exaggerate claims of effectiveness. Finally, extrapolating from research settings to clinical settings should more rigorously take into account possible ways in which the intervention in question can fail to be effective in a target population. (shrink)

To be effective, a medical intervention must improve one's health by targeting a disease. The concept of disease, though, is controversial. Among the leading accounts of disease-naturalism, normativism, hybridism, and eliminativism-I defend a version of hybridism. A hybrid account of disease holds that for a state to be a disease that state must both (i) have a constitutive causal basis and (ii) cause harm. The dual requirement of hybridism entails that a medical intervention, to be deemed effective, must target either (...) the constitutive causal basis of a disease or the harms caused by the disease (or ideally both). This provides a theoretical underpinning to the two principle aims of medical treatment: care and cure. (shrink)

Francesco Guala once wrote that ‘The problem of extrapolation is a minor scandal in the philosophy of science’. This paper agrees with the statement, but for reasons different from Guala’s. The scandal is not, or not any longer, that the problem has been ignored in the philosophy of science. The scandal is that framing the problem as one of external validity encourages poor evidential reasoning. The aim of this paper is to propose an alternative—an alternative which constitutes much better evidential (...) reasoning about target systems of interest, and which makes do without consideration of external validity. (shrink)

This paper develops an account of the opacity problem in medical machine learning (ML). Guided by pragmatist assumptions, I argue that opacity in ML models is problematic insofar as it potentially undermines the achievement of two key purposes: ensuring generalizability and optimizing clinician–machine decision-making. Three opacity amelioration strategies are examined, with explainable artificial intelligence (XAI) as the predominant approach, challenged by two revisionary strategies in the form of reliabilism and the interpretability by design. Comparing the three strategies, I argue that (...) interpretability by design is most promising to overcome opacity in medical ML. Looking beyond the individual opacity amelioration strategies, the paper also contributes to a deeper understanding of the problem space and the solution space regarding opacity in medical ML. (shrink)

Simple extrapolation is the orthodox approach to extrapolating from clinical trials in evidence-based medicine: extrapolate the relative effect size from the trial unless there is a compelling reason not to do so. I argue that this method relies on a myth and a fallacy. The myth of simple extrapolation is the idea that the relative risk is a ‘golden ratio’ that is usually transportable due to some special mathematical or theoretical property. The fallacy of simple extrapolation is an unjustified argument (...) from ignorance: we conclude that the relative effect size is transportable in the absence of evidence to the contrary. In short, simple extrapolation is a deeply problematic solution to the problem of extrapolation. (shrink)

Over the past several decades, we devoted much energy to generating, reviewing and summarizing evidence. We have given far less attention to the issue of how to thoughtfully apply the evidence once we have it. That’s fine if all we care about is that our clinical decisions are evidence-based, but not so good if we also want them to be well-reasoned. Let us not forget that evidence based medicine (EBM) grew out of an interest in making medicine ‘rational’, with the (...) idea that rational clinical evaluations should be evidence-based. I agree with the uncontroversial statement that the best decision is supported, at least in part, by the best available evidence. Rationality, however, is constituted by reasoning, not evidence. Complete arguments are necessary for rational evaluations, arguments that begin with general evidence and end in a conclusion about a particular patient. In order to traverse these inferential gaps, medicine must address the issue of how to establish, as an intermediate premise, what the evidence has to say about the efficacy of an intervention for particular patients in a particular practice setting. (shrink)

Reasoning in medicine requires the critical use of a clinical methodology whose validity must be evaluated as well as its limits. In the last decade, an increasing amount of evidence has shown severe limitations and flaws in the conduct of prognostic studies. The main reason behind this fact is that prognostic judgments are at high risk of error. In this paper we investigate the pragmatic and illocutionary aspects of different forms of linguistic acts and judgments involved in clinical practice. More (...) specifically, we analyze the role of uncertainty with regard to ‘particular’ clinical judgments and its relation with ‘general’ evidence. Focusing on how prognostic judgments are formulated and justified, our main purpose is to highlight the explication, the structure and the limits of prognosis from a linguistic and epistemological perspective. (shrink)