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

Pretis, Francesco De; Landes, Jürgen und Osimani, Barbara (2019): E-Synthesis: A Bayesian Framework for Causal Assessment in Pharmacosurveillance. In: Frontiers in Pharmacology, Bd. 10, 1317

Abstract

Background: 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.

Methods: 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. In this article, we 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.

Results: 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 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.

Conclusions: E-Synthesis is highly flexible concerning the allowed input, while at the same time relying on a consistent computational system, that is 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.

• Dokumententyp: Zeitschriftenartikel
• EU Funded Grant Agreement Number: 639276
• EU-Projekte: Horizon 2020 > ERC Grants > ERC Starting Grant > ERC Grant 639276: PhilPharm - Philosophy of Pharmacology: Safety, Statistical Standards and Evidence Amalgamation
• Keywords: Adverse drug reaction; drug safety; causal assessment; Bradford Hill Guidelines; statistical evidence; evidence; synthesis; evidence quality; pharmacovigilance; pharmacosurveillance
• Fakultät: Philosophie, Wissenschaftstheorie und Religionswissenschaft > Munich Center for Mathematical Philosophy (MCMP) > Epistemology
• Themengebiete: 100 Philosophie und Psychologie > 100 Philosophie ; 100 Philosophie und Psychologie > 120 Epistemologie
• ISSN: 1663-9812
• Sprache: Englisch