Pharmaceutical decisions are affected by several forms of uncertainty, which are sharpened both by the high stakes at play and by the complexity of the epistemological procedures needed to provide the necessary information to make these decisions. The precautionary principle as applied to pharmaceutical decisions through the notion of ‘well-founded suspicion’ takes into account one special sort of these uncertainties: the uncertainty concerning the causal connection between observed adverse reactions and the drug suspected of causing these effects. Commentators have criticised the precautionary principle for its inhibitory action on innovation and research, for its unjustified imbalance towards the risk produced by human agency versus natural risks; and from a formal point of view, for its vagueness and unsystematic application. Their criticisms have generally been grounded in the supposed risk-aversive nature of the precautionary principle. The purpose of this article is to address these criticisms. In this article, Iengage in a discussion of the current methodological debate about epistemic asymmetries concerning standards of evidence for pharmaceutical harm and benefits to examine the rationale underpinning the logic of the precautionary principle. I show that the precautionary principle has been developed as a means of acknowledging uncertainty, and therefore, the basis of its implementation should be based on inductive rather than deductive approaches to scientific enquiry.

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.

Contemporary debates about scientific institutions and practice feature many proposed reforms. Most of these require increased efforts from scientists. But how do scientists’ incentives for effort interact? How can scientific institutions encourage scientists to invest effort in research? We explore these questions using a game-theoretic model of publication markets. We employ a base game between authors and reviewers, before assessing some of its tendencies by means of analysis and simulations. We compare how the effort expenditures of these groups interact in our model under a variety of settings, such as double-blind and open review systems. We make a number of findings, including that open review can increase the effort of authors in a range of circumstances and that these effects can manifest in a policy-relevant period of time. However, we find that open review’s impact on authors’ efforts is sensitive to the strength of several other influences.

Background: Basic science has delivered unprecedented insights into intricate relationships on the smallest scales within well-controlled environments. Addressing pressing societal decision problems requires an understanding of systems on larger scales in real-world situations. Objective: To assess how well the evidence assessors E-Synthesis and EBM+ assess basic science findings to support medical decision making. Method: We demonstrate the workings of E-Synthesis and EBM+ on a case study: the suspected causal connection between the widely-used drug amoxicillin (AMX) and the putative adverse drug reaction: Drug Reaction with Eosinophilia and Systemic Symptoms (DRESS). Results: We determine an increase in the probability that AMX can cause DRESS within the E-Synthesis approach and using the EBM+ standards assess the basic science findings as supporting the existence of a mechanism linking AMX and DRESS. Conclusions: While progress is made towards developing methodologies which allow the incorporation of basic science research in the decision making process for pressing societal questions, there is still considerable need for further developments. A continued dialogue between basic science researchers and methodologists, philosophers and statisticians seems to offer the best prospects for developing and evaluating continuously evolving methodologies.