De Pretis, Francesco and Landes, Juergen and Peden, William and Osimani, Barbara
(2021)
Pharmacovigilance as Personalized Medicine. In: Chiara Beneduce and Marta Bertolaso (eds.) Personalized Medicine: A Multidisciplinary Approach to Complexity, Springer Nature.
[Preprint]
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.
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