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)

The article by MacKay and Saylor (2020) claims that the principle of fair subject selection yields conflicting imperatives (e.g. in the case of pregnant women) and should be understood as “a bundle of four distinct sub-principles” (i.e. fair inclusion, burden sharing, opportunity, distribution of third-party risks), each having conflicting normative recommendations (MacKay and Saylor 2020). The authors also offer guidance as to how we should navigate between subprinciples that may conflict with each other. The problem is a crucial one since (...) fair subject selection is one of the principles regulating clinical research that produces generalizable knowledge with the potential of improving people’s health. Therefore, there may be cases where the way in which participants are selected directly influences the generalizability (or its lack) of the clinically relevant knowledge and its value (if any) to different groups. In my commentary article, written from the philosophical perspective, I notice a number of interrelated problems which I believe have not been discussed thoroughly in the target article: (1) the precise way in which health care priority setting should influence the content of health research priority setting and fair inclusion principles; (2) the distinction between group and individual benefits and burdens from clinical research; (3) the reference class problem in medical research. (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)

My doctoral project is a study of epistemological and ethical issues in Evidence-Based Medicine, a movement in medicine which emphasizes the use of randomized controlled trials. Much of the research on EBM suggests that, for a large part of the movement's history, EBM considered expertise, mechanisms, and values to be forces contrary to its goals and has sought to remove them, both from medical research and from the clinical encounter. I argue, however, that expertise, mechanisms and values have important epistemological (...) and ethical roles to play and can be incorporated into the current EBM movement. (shrink)

Heterogeneous treatment effects represent a major issue for medicine as they undermine reliable inference and clinical decision-making. To overcome the issue, the current vision of precision and personalized medicine acknowledges the need to control individual variability in response to treatment. In this paper, we argue that gene-treatment-environment interactions (G × T × E) undermine inferences about individual treatment effects from the results of both genomics-based methodologies—such as genome-wide association studies (GWAS) and genome-wide interaction studies (GWIS)—and randomized controlled trials (RCTs). Then, (...) we argue that N-of-1 trials can be a solution to overcome difficulties in handling individual variability in treatment response. Although this type of trial has been suggested as a promising strategy to assess individual treatment effects, it nonetheless has limitations that limit its use in everyday clinical practice. We analyze the existing variability within the designs of N-of-1 trials in terms of a continuum where each design prioritizes epistemic and pragmatic considerations. We then support wider use of the designs located at the pragmatic end of the explanatory-pragmatic continuum. (shrink)

Volume 20, Issue 2, February 2020, Page 20-22.

The study of rare diseases has long been an area of challenge for medical researchers, with agonizingly slow movement towards improved understanding of pathophysiology and treatments compared with more common illnesses. The push towards evidence-based medicine (EBM), which prioritizes certain types of evidence over others, poses a particular issue when mapped onto rare diseases, which may not be feasibly investigated using the methodologies endorsed by EBM, due to a number of constraints. While other trial designs have been suggested to overcome (...) these limitations (with varying success), perhaps the most recent and enthusiastically adopted is the application of artificial intelligence to rare disease data. This paper critically examines the pitfalls of EBM (and its trial design offshoots) as it pertains to rare diseases, exploring the current landscape of AI as a potential solution to these challenges. This discussion is also taken a step further, providing philosophical commentary on the weaknesses and dangers of AI algorithms applied to rare disease research. While not proposing a singular solution, this article does provide a thoughtful reminder that no ‘one-size-fits-all’ approach exists in the complex world of rare diseases. We must balance cautious optimism with critical evaluation of new research paradigms and technology, while at the same time not neglecting the ever-important aspect of patient values and preferences, which may be challenging to incorporate into computer-driven models. (shrink)

Establishing the effectiveness of medical treatments is one of the most important aspects of medical practice. Bradford Hill's viewpoints play an important role in inferring causality in medicine,...

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)