Personalized medicine relies on two points: (1) causal knowledge about the possible effects of X in a given statistical population; (2) assignment of the given individual to a suitable reference class. Regarding point 1, standard approaches to causal inference are generally considered to be characterized by a trade-off between how confidently one can establish causality in any given study (internal validity) and extrapolating such knowledge to specific target groups (external validity). Regarding point 2, it is uncertain which reference class leads to the most reliable inferences. Instead, pharmacovigilance focuses on both elements of the individual prediction at the same time, that is, the establishment of the possible causal link between a given drug and an observed adverse event, and the identification of possible subgroups, where such links may arise. We develop an epistemic framework that exploits the joint contribution of different dimensions of evidence and allows one to deal with the reference class problem not only by relying on statistical data about covariances, but also by drawing on causal knowledge. That is, the probability that a given individual will face a given side effect, will probabilistically depend on his characteristics and the plausible causal models in which such features become relevant. The evaluation of the causal models is grounded on the available evidence and theory.

Pharmacovigilance as Personalized Evidence / F. De Pretis, W. Peden, J. Landes, B. Osimani (HUMAN PERSPECTIVES IN HEALTH SCIENCES AND TECHNOLOGY). - In: Personalized Medicine in the Making : Philosophical Perspectives from Biology to Healthcare / [a cura di] C. Beneduce, M. Bertolaso. - [s.l] : Springer Cham, 2022. - ISBN 978-3-030-74803-6. - pp. 147-171 [10.1007/978-3-030-74804-3_8]

Pharmacovigilance as Personalized Evidence

J. Landes;
2022

Abstract

Personalized medicine relies on two points: (1) causal knowledge about the possible effects of X in a given statistical population; (2) assignment of the given individual to a suitable reference class. Regarding point 1, standard approaches to causal inference are generally considered to be characterized by a trade-off between how confidently one can establish causality in any given study (internal validity) and extrapolating such knowledge to specific target groups (external validity). Regarding point 2, it is uncertain which reference class leads to the most reliable inferences. Instead, pharmacovigilance focuses on both elements of the individual prediction at the same time, that is, the establishment of the possible causal link between a given drug and an observed adverse event, and the identification of possible subgroups, where such links may arise. We develop an epistemic framework that exploits the joint contribution of different dimensions of evidence and allows one to deal with the reference class problem not only by relying on statistical data about covariances, but also by drawing on causal knowledge. That is, the probability that a given individual will face a given side effect, will probabilistically depend on his characteristics and the plausible causal models in which such features become relevant. The evaluation of the causal models is grounded on the available evidence and theory.
Drug safety; Personalized medicine; Pharmacosurveillance; Pharmacovigilance; Precision medicine
Settore M-FIL/02 - Logica e Filosofia della Scienza
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/948199
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