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Hypothesis and Theory ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Pharmacol. | doi: 10.3389/fphar.2019.01317

E-Synthesis: a Bayesian Framework for Causal Assessment in Pharmacosurveillance

Francesco De Pretis1, Jürgen Landes2 and  Barbara Osimani1, 2*
  • 1Marche Polytechnic University, Italy
  • 2Ludwig Maximilian University of Munich, Germany

Evidence suggesting adverse drug reactions often emerges unsystematically and unpredictably in form of anecdotal reports, case series and survey data. Safety trials and observational studies also provide crucial information regarding the (un-)safety of drugs. Hence, integrating multiple types of pharmacovigilance evidence is key to minimising the risks of harm.

In previous work, we began the development of a Bayesian framework for aggregating multiple types of evidence to assess the probability of a putative causal link between drugs and side effects. This framework arose out of a philosophical analysis of the Bradford Hill Guidelines. We here expand the Bayesian framework and add "evidential modulators", which bear on the assessment of the reliability of incoming study results. The overall framework for evidence synthesis, "E-Synthesis" is then applied to a case study.

Theoretically and computationally, E-Synthesis exploits coherence of partly or fully independent evidence converging towards the hypothesis of interest (or of conflicting evidence with respect to it), in order to update its posterior probability. With respect to other frameworks for evidence synthesis, our Bayesian model has the unique feature of grounding its inferential machinery on a consolidated theory of hypothesis confirmation (Bayesian epistemology), and in allowing any data from the most heterogeneous sources (cell-data, clinical trials, epidemiological studies), and methods (e.g., frequentist hypothesis testing, Bayesian adaptive trials, etc.) to be quantitatively integrated into the same inferential framework.

E-Synthesis is at the same time highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, philosophically and statistically grounded.
Furthermore, by introducing evidential modulators, and thereby breaking up the different dimensions of evidence (strength, relevance, reliability), E-Synthesis allows them to be explicitly tracked in updating causal hypotheses.

Keywords: Bayesian Belief Networks, Pharmacosurveillance, causal inference, safety signals, Bradford hill criteria

Received: 14 Feb 2019; Accepted: 15 Oct 2019.

Copyright: © 2019 De Pretis, Landes and Osimani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Barbara Osimani, Marche Polytechnic University, Ancona, Italy,