Search

The increasing success of the evidence-based policy movement is raising the demand of empirically informed decision making. As arguably any policy decision happens under conditions of uncertainty, following our best available evidence to reduce the uncertainty seems a requirement of good decision making. However, not all the uncertainty faced by decision makers can be resolved by evidence. In this paper, we build on a philosophical analysis of uncertainty to identify the boundaries of scientific advice in policy decision making. We start by introducing a distinction between empirical and non-empirical types of uncertainty, and we explore the role of two non-empirical uncertainties in the context of policy making. We argue that the authority of scientific advisors is limited to empirical uncertainty and cannot extend beyond it. While the appeal of evidence-based policy rests on a view of scientific advice as limited to empirical uncertainty, in practice there is a risk of over reliance on experts beyond the legitimate scope of their authority. We conclude by applying our framework to a real-world case of evidence-based policy, where experts have overstepped their boundaries by ignoring non-empirical types of uncertainty.

Replicability is widely regarded as one of the defining features of science and its pursuit is one of the main postulates of meta-research, a discipline emerging in response to the replicability crisis. At the same time, replicability is typically treated with caution by philosophers of science. In this paper, we reassess the value of replicability from an epistemic perspective. We defend the orthodox view, according to which replications are always epistemically useful, against the more prudent view that claims that it is useful in very limited circumstances. Additionally, we argue that we can learn more about the original experiment and the limits of the discovered effect from replications at different levels. We hold that replicability is a crucial feature of experimental results and scientists should continue to strive to secure it.

Among the philosophies of special sciences, Philosophy of Medicine is an emerging field, even though the relationship between philosophy and medicine dates back to ancient times. Since the 1980s, t...

Since the advent of drug regulation in 1962, regulatory agencies have been in the practice of using strict standards to test the safety and efficacy of medical treatments and products. Regulatory agencies, such as the FDA, demand two full-fledged Randomized Clinical Trials demonstrating the safety and effectiveness of drugs to grant its marketing authorization. On the contrary, surgical treatments are left completely unregulated. There are several reasons explaining this difference, and all of them point to the difficulty of conducting well-designed RCTs in surgery. However, we argue that none of these arguments is decisive and that, under certain conditions, surgical RCTs can be morally justified and methodologically sound. Although ethical constraints restrict the number of testable surgical procedures, and surgical trials might not be as dependable as pharmaceutical RCTs, our analysis suggests that, in certain cases, it is possible to obtain high-quality evidence about the safety and efficacy of surgical procedures. Untested surgical treatments may prove to be ineffective and harm patients. Therefore, regulation of surgical procedures seems not only morally acceptable and able to provide reliable scientific evidence, but also desirable and justified from an ethical-political standpoint., Theoretical Medicine and Bioethics, 43 (2), ISSN:1573-1200, ISSN:1386-7415

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 (fundamental) 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.

Over the last decade, philosophers of science have extensively criticized the epistemic superiority of randomized controlled trials (RCTs) 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.

Science progresses through debate and disagreement, and scientific controversies play a crucial role in the growth of scientific knowledge. However, not all controversies and disagreements are progressive in science. Sometimes, controversies can be pseudoscientific; in fact, bogus controversies, and what seem like genuine scientific disagreements, can be a distortion of science set up by non-scientific actors (e.g., interest groups). Bogus controversies are detrimental to science because they can hinder scientific progress and eventually bias science-based decisions. The first goal of this paper is to elucidate the distinction between bogus and genuine scientific controversies and provide a qualitative methodology, based on the literature on expertise, for distinguishing between the two. We will illustrate six epistemic criteria for distinguishing bogus from genuine scientific debates in science and medicine. This heuristic strategy applies directly to scientific reports, and it relies mostly on the social structure of science. We will then apply the above criteria to a case study: the controversy over statins, which are widely prescribed drugs for reducing the level of cholesterol and preventing cardiovascular disease.

Medical research makes intensive use of statistics in order to support its claims. In this paper we make explicit an epistemological tension between the conduct of clinical trials and their interpretation: statistical evidence is sometimes discarded on the basis of an (often) underlined Bayesian reasoning. We suggest that acknowledging the potentiality of Bayesian statistics might contribute to clarify and improve comprehension of medical research. Nevertheless, despite Bayesianism may provide a better account for scientific inference with respect to the standard frequentist approach, Bayesian statistics is rarely adopted in clinical research. The main reason lies in the supposed subjective elements characterizing this perspective. Hence, we discuss this objection presenting the so-called Reference analysis, a formal method which has been developed in the context of objective Bayesian statistics in order to define priors which have a minimal or null impact on posterior probabilities. Furthermore, according to this method only available data are relevant sources of information, so that it resists the most common criticisms against Bayesianism.

In the last decade, robustness has been extensively mentioned and discussed in biology as well as in the philosophy of the life sciences. Nevertheless, from both fields, someone has affirmed that this debate has resulted in more semantic confusion than in semantic clearness. Starting from this claim, we wish to offer a sort of prima facie map of the different usages of the term. In this manner we would intend to predispose a sort of “semantic platform” which could be exploited by those who wish to discuss or simply use it. We do this by starting from a core distinction between the robustness of representations, which is a philosophy of science issue, and the representations of robustness, which instead pertains to science. We illustrate our proposal with examples from biology, physics and mathematics.

Testing new oncological treatments in the era of personalized medicine is raising many challenges to the current regulatory paradigm. In particular, randomized controlled trials (RCTs) have proved to be inadequate for testing targeted therapies. Nonetheless, the Food and Drug Administration (FDA) still requires them to grant market approval. This article questions the idea that regulatory decision-making can be reduced to sound statistical inferences. The author discusses the implications of tumor heterogeneity for regulatory assessment of new medications, considering the challenges that the current paradigm is facing and addressing criticisms that could explain its resistance to change. To overcome those criticisms, the author proposes implementing casuistic method into regulatory decision-making.

In the last century, use of statistics became widespread in biomedical research, making medicine more scientific. However, the misuse of statistical methods may have a negative impact on the reproducibility of experiments, which instead should be at the core of scientific method. The consequence is the so called "replicability crisis", which can affect the reliability of scientific results: if one cannot replicate a published finding, she would hardly trust it. In this paper, firstly, we will provide a short review of the replicability crisis. Indeed, this issue has recently drawn the attention of the entire scientific community. Secondly, we will try to figure out some potential explanation for this phenomenon. Finally, we will show how this problem deeply affects also the reliability of randomized clinical trial, which is fundamental for the medical and regulatory decision-making.

In the last few years, the lack of a unitary notion of gene across biological sciences has troubled the philosophy of biology community. However, the debate on this concept has remained largely historical or focused on particular cases presented by the scientific empirical advancements. Moreover, in the literature there are no explicit and reasonable arguments about why a philosophical clarification of the concept of gene is needed. In our paper, we claim that a philosophical clarification of the concept of gene does not contribute to biology. Unlike the question, for example, "What is a biological function?", we argue that the question "What is a gene?" could be answered by means of empirical research, in the sense that biologists' labour is enough to shed light on it.

Hooks et al. note that microbiota-gut-brain research suffers from serious methodological flaws and interpretative issues. We suggest two corrective measures: first, taking more seriously the need of interdisciplinary work; second, interpreting some of the methodological issues as ordinary challenges of standardization, typical of emerging disciplines.