Jacob Stegenga

I defend a radical interpretation of biological populations—what I call population pluralism—which holds that there are many ways that a particular grouping of individuals can be related such that the grouping satisfies the conditions necessary for those individuals to evolve together. More constraining accounts of biological populations face empirical counter-examples and conceptual difficulties. One of the most intuitive and frequently employed conditions, causal connectivity—itself beset with numerous difficulties—is best construed by considering the relevant causal relations as ‘thick’ causal concepts. I argue that the fine-grained causal relations that could constitute membership in a biological population are huge in number and many are manifested by degree, and thus we can construe population membership as being defined by massively multidimensional constructs, the differences between which are largely arbitrary. I end by showing that positions in two recent debates in theoretical biology depend on a view of biological populations at odds with the pluralism defended here.

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.

The chemical characterization of the substance responsible for the phenomenon of “transformation” of pneumococci was presented in the now famous 1944 paper by Avery, MacLeod, and McCarty. Reception of this work was mixed. Although interpreting their results as evidence that deoxyribonucleic acid (DNA) is the molecule responsible for genetic changes was, at the time, controversial, this paper has been retrospectively celebrated as providing such evidence. The mixed and changing assessment of the evidence presented in the paper was due to the work’s interpretive flexibility – the evidence was interpreted in various ways, and such interpretations were justified given the neophytic state of molecular biology and methodological limitations of Avery’s transformation studies. I argue that the changing context in which the evidence presented by Avery’s group was interpreted partly explains the vicissitudes of the assessments of the evidence. Two less compelling explanations of the reception are a myth-making account and an appeal to the wartime historical context of its publication.

An astonishing volume and diversity of evidence is available for many hypotheses in the biomedical and social sciences. Some of this evidence—usually from randomized controlled trials (RCTs)—is amalgamated by meta-analysis. Despite the ongoing debate regarding whether or not RCTs are the ‘gold-standard’ of evidence, it is usually meta-analysis which is considered the best source of evidence: meta-analysis is thought by many to be the platinum standard of evidence. However, I argue that meta-analysis falls far short of that standard. Different meta-analyses of the same evidence can reach contradictory conclusions. Meta-analysis fails to provide objective grounds for intersubjective assessments of hypotheses because numerous decisions must be made when performing a meta-analysis which allow wide latitude for subjective idiosyncrasies to influence its outcome. I end by suggesting that an older tradition of evidence in medicine—the plurality of reasoning strategies appealed to by the epidemiologist Sir Bradford Hill—is a superior strategy for assessing a large volume and diversity of evidence.

WE AIM HERE to outline a theory of evidence for use. More specifically we lay foundations for a guide for the use of evidence in predicting policy effectiveness in situ, a more comprehensive guide than current standard offerings, such as the Maryland rules in criminology, the weight of evidence scheme of the International Agency for Research on Cancer (IARC), or the US ‘What Works Clearinghouse’. The guide itself is meant to be well-grounded but at the same time to give practicable advice, that is, advice that can be used by policy-makers not expert in the natural and social sciences, assuming they are well-intentioned and have a reasonable but limited amount of time and resources available for searching out evidence and deliberating.

Robustness is a common platitude: hypotheses are better supported with evidence generated by multiple techniques that rely on different background assumptions. Robustness has been put to numerous epistemic tasks, including the demarcation of artifacts from real entities, countering the “experimenter’s regress,” and resolving evidential discordance. Despite the frequency of appeals to robustness, the notion itself has received scant critique. Arguments based on robustness can give incorrect conclusions. More worrying is that although robustness may be valuable in ideal evidential circumstances (i.e., when evidence is concordant), often when a variety of evidence is available from multiple techniques, the evidence is discordant. †To contact the author, please write to: Jacob Stegenga, Department of Philosophy, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093; e‐mail: [email protected].

Harms of medical interventions are systematically underestimated in clinical research. Numerous factors—conceptual, methodological, and social—contribute to this underestimation. I articulate the depth of such underestimation by describing these factors at the various stages of clinical research. Before any evidence is gathered, the ways harms are operationalized in clinical research contributes to their underestimation. Medical interventions are first tested in phase 1 ‘first in human’ trials, but evidence from these trials is rarely published, despite the fact that such trials provide the foundation for assessing the harm profile of medical interventions. If a medical intervention is deemed safe in a phase 1 trial, it is tested in larger phase 2 and 3 clinical trials. One way to think about the problem of underestimating harms is in terms of the statistical ‘power’ of a clinical trial—the ability of a trial to detect a difference of a certain effect size between the experimental group and the control group. Power is normally thought to be pertinent to detecting benefits of medical interventions. It is important, though, to distinguish between the ability of a trial to detect benefits and the ability of a trial to detect harms. I refer to the former as power-B and the latter as power-H. I identify several factors that maximize power-B by sacrificing powerH in phase 3 clinical trials. If a medical intervention is approved for general use, it is evaluated by phase 4 post-market surveillance. Phase 4 surveillance of harms further contributes to underestimating the harm profile of medical interventions. At every stage of clinical research the hunt for harms is shrouded in secrecy, which further contributes to the underestimation of the harm profiles of medical interventions.

Medical scientists employ ‘quality assessment tools’ (QATs) to measure the quality of evidence from clinical studies, especially randomized controlled trials (RCTs). These tools are designed to take into account various methodological details of clinical studies, including randomization, blinding, and other features of studies deemed relevant to minimizing bias and error. There are now dozens available. The various QATs on offer differ widely from each other, and second-order empirical studies show that QATs have low inter-rater reliability and low inter-tool reliability. This is an instance of a more general problem I call the underdetermination of evidential significance. Disagreements about the strength of a particular piece of evidence can be due to different—but in principle equally good—weightings of the fine-grained methodological features which constitute QATs.