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

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.

On the Assessed Strength of Agents’ Bias

“…Their model has found applications in the philosophy of science concerning the epistemological Variety of Evidence Thesis (Bovens and Hartmann 2002;Claveau 2013;Claveau and Grenier 2019;Stegenga and Menon 2017;Landes 2020b, a), which states that varied evidence for a hypothesis confirms it more strongly than less varied evidence, ceteris paribus. Furthermore, it has been employed in Hahn et al (2016) for modelling social debates of findings in climate science, the philosophy of economics Casini and Landes (2020) and (the philosophy of) medicine (Abdin et al 2019;Landes et al 2018;De Pretis et al 2019;.…”

New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment

“…E-Synthesis emerges as a theoretical framework for causal assessment based on [20,21], aimed to support estimates of causal associations between drugs and adverse events. E-Synthesis practically consists in a “scaffold” where all the available evidence may probabilistically increase or decrease causal hypotheses.…”

“…In [21] we presented the epistemic dynamics of E-Synthesis by applying it to a case study: The debated causal association between paracetamol and asthma. We focus here on the DR arm.…”

“…During 297,282 person-years of follow-up, 299 participants reported a new physician diagnosis of asthma meeting diagnostic criteria (see Table 1). For this study, in [21] we derived:P(ES=1|truex→,DR,T)=1 and:P(ES=1|truex→,DR¯,T¯)=0 where ES stands for the observed effect size (with significant = 1, not significant = 0) and xfalse→ is a vector containing all the modulators introduced in the previous part. Evaluating such research study allows one to quantify the probability of witnessing a proxy of hypothesis of causation (i.e., the effect size, in this case) conditioned on the presence of dose-responsiveness and temporal precedence.…”

“…This article presents a new theoretical framework to support decisions in pharmacovigilance, defined “ E-Synthesis ”, a tool encoded through a Bayesian network able to robustly screen and amalgamate evidence of different types (such as epidemiological studies, molecular biology research findings, case reports) that are combined together in order to form the “critical mass” needed to make decisions about the approval/withdrawal of a particular drug for/from the market [20,21].…”