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Philosophical Aspects of Evidence and Methodology in Medicine

Time: Mon 2021-05-17 09.00

Location: Videolänk, Du som saknar dator /datorvana kontakta [SAKNAS / MISSING] / Use the e-mail address if you need technical assistance, Stockholm (English)

Subject area: Philosophy

Doctoral student: Jesper Jerkert , Filosofi

Opponent: Senior lecturer Adam La Caze, University of Queensland, Brisbane, Australia

Supervisor: Professor John Cantwell, Filosofi och historia; Professor emeritus Sven Ove Hansson, Filosofi; Universitetslektor Tor Sandqvist, Filosofi


The thesis consists of an introduction and five papers. The introduction gives a brief historical survey of empirical investigations into the effectiveness of medicinal interventions, as well as surveys of the concept of evidence and of the history and philosophy of experiments. The main ideas of the EBM (evidence-based medicine) movement are also presented.

Paper I: Concerns have been raised that clinical trials do not offer reliable evidence for some types of treatment, in particular for highly individualised treatments, for example traditional homeopathy. With respect to individualised treatments, it is argued that such concerns are unfounded. There are two minimal conditions related to the nature of the treatments that must be fulfilled for evaluability in a clinical trial, namely (1) the proper distinction of treatment groups and (2) the elimination of confounding variables or variations. These conditions do not preclude the testing of individualised medicine.

Paper II: Traditionally, mechanistic reasoning has been assigned a negligible role in the EBM literature. When discussed, mechanistic reasoning has almost exclusively been positive – both in an epistemic sense of claiming that there is a mechanistic chain and in a health-related sense of there being claimed benefits for the patient. Negative mechanistic reasoning has been neglected. I distinguish three main types of negative mechanistic reasoning and subsume them under a new definition. One of the three distinguished types, which is negative only in the health-related sense, has a corresponding positive counterpart, whereas the other two, which are epistemically negative, do not have such counterparts, at least not that are particularly interesting as evidence. Accounting for negative mechanistic reasoning in EBM is therefore partly different from accounting for positive mechanistic reasoning.

Paper III: Evidence hierarchies are lists of investigative strategies ordered with regard to the claimed strength of evidence. They have been used for a couple of decades within EBM, particularly for the assessment of evidence for treatment recommendations, but they remain controversial. An under-investigated question is what the order in the hierarchy means. Four interpretations of the order are distinguished and discussed. The two most credible ones are, in rough terms, “typically stronger” and “ideally stronger”. The GRADE framework seems to be based on the “typically stronger” reading. Even if the interpretation of an evidence hierarchy were established, hierarchies appear to be rather unhelpful for the task of evidence aggregation. However, specifying the intended order relation may help sort out disagreements.

Paper IV: There are three main arguments for randomisation that connect inseparably to theoretical concepts: (1) Randomisation is useful for performing null hypothesis testing. (2) Randomisation is needed for plausible causal inferences from treatment to effect. (3) Randomisation is acceptable and computationally convenient in a Bayesian setting. A critical scrutiny of these arguments shows that (1) is acceptable in the context of clinical trials. As for (2), it is argued that randomisation only provides weak reasons for drawing causal inferences in the context of real (as opposed to theoretically ideal but unrealistic) clinical trials. Argument (3) is weak because it is controversial among Bayesians, and because formally Bayesian analyses of trial results are rarely asked for.

Paper V: Practical arguments for randomisation are arguments with no necessary connections to theoretical frameworks like null hypothesis testing or causal inferences. Four common practical arguments in the context of clinical trials are distinguished and assessed: (1) Randomisation contributes to allocation concealment. (2) Randomisation contributes to the baseline balance of treatment groups. (3) Randomisation decreases self-selection bias. (4) Randomisation removes allocation bias. Argument (1) is rejected. Arguments (3) and (4) are approved. Argument (2) is rejected if it is formulated so as to be independent from (3) and (4), but it is true that randomisation contributes to balance through the mechanisms mentioned in (3) and (4). It is judged that (4) may be the strongest single argument.

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Last changed: May 16, 2021