Collecting and evaluating evidence on pharmaceutical safety is a central problem in health-care practice. Adverse Drug Reactions (ADRs) are responsible for a heavy economic and social burden (Edwards and Lundkvist 2017) and constitute a vulnerable point for the health system, a key ethical problem and a top-priority (also in the current Covid-19 pandemic: Chandler et al. 2020). In view of these considerations, the European regulation for pharmacovigilance practice (Directive 2010/84/EU; Regulation (EU) No 1235/2010) has put a special emphasis on joint efforts for an information-based approach to pharmaceutical risk assessment. The related guidelines encourage the integration of information coming from different sources of safety signals. In the U.S. the Congress approved Pub.L. 114 - 255 (21st Century Cures Act) in 2016, thereby allowing companies to provide “data summaries” and “real world evidence” such as observational studies, insurance claims data, patient input, and anecdotal data rather than exclusively relying on random clinical trials (RCTs) data, for the purpose of drug approval and evaluation. Yet the methodological bases for implementing such policies are shaky, in that causal assessment of ADRs is still parasitic on the (statistical) methods developed to test drug efficacy — focused on hypothesis testing, rather than detection, and therefore inadequate to manage uncertainty.
The PhilPharm project addressed this problem by developing a theoretical framework for probabilistic confirmation of causal hypotheses on the basis of all the available evidence on safety for medical treatments: “E-Synthesis”, along three tightly interconnected strands of research — numbers refer to the papers listed in the electronic submission system; letters to forthcoming papers, listed in part 1.7 of the “Project Achievements” section.
I. The elaboration of a theoretical framework that responds to the following objectives:
1) foundational analysis on statistical/causal inference in pharmacosurveillance;
2) theoretical analysis of standards of drug evaluation;
3) a unified epistemic framework, within which different kinds of evidence can be combined and used for decision:
E-Synthesis: 4, 9, 20, 21, 23, 35.
II. Formal analysis of scientific inference, with a focus on higher order dimensions of evidence: reliability (13, 28, 39, 42, forthcoming: d), variety (10, 41, 42, 47 and forthcoming: a, b, j), coherence (4, 27, 10, 46); as well as decision-theoretic and methodological issues (1, 2, 3, 4, 9, 14); a series of papers analysing foundational aspects of scientific inference from the perspective logics of variable inclusion (22, 24, 25, 26, 27).
III. Formal analysis of strategic dimensions of scientific interactions in the sciences, with a special focus on evidence disclosure in medical and pharmacological settings (forthcoming: e, i).
