Philosophical discussions on causal inference in medicine are stuck in dyadic camps, each defending one kind of evidence or method rather than another as best support for causal hypotheses. Whereas Evidence Based Medicine advocates invoke the use of Randomised Controlled Trials and systematic reviews of RCTs as gold standard, philosophers of science emphasise the importance of mechanisms and their distinctive informational contribution to causal inference and assessment. Some have suggested the adoption of a pluralistic approach to causal inference, and an inductive rather than hypothetico-deductive inferential paradigm. However, these proposals deliver no clear guidelines about how such plurality of evidence sources should jointly justify hypotheses of causal associations. In this paper, we develop the pluralistic approach along Hill's (1965) famous criteria for discerning causal associations by employing Bovens' and Hartmann's general Bayes net reconstruction of scientific inference to model the assessment of harms in an evidenceamalgamation framework.

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.

Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities; they should be calibrated to our evidence of physical probabilities; and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism are usually justified in different ways. In this paper, we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes' Theorem.

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Objective Inhibition of nitric oxide (NO) produced by inducible NO synthase (iNOS) is suggested to be beneficial in experimental arthritis. Although NO is important for the integrity of the microcirculation, the effects of inhibition of iNOS on the synovial microcirculation are not currently known. This study investigated the synovial microcirculation and leukocyte–endothelial cell interactions in iNOS‐deficient mice with antigen‐induced arthritis (AIA) and compared these findings with disease severity. Methods Fourteen homozygous iNOS−/− and 14 iNOS+/+ mice were used. The severity of AIA was assessed by measuring knee joint swelling and by histologic scoring. The number of rolling and adherent leukocytes was quantitatively analyzed in synovial microvessels using intravital microscopy of intraarticular synovial tissue. Nitrite/nitrate concentrations were measured, and the expression of iNOS, E‐ and P‐selectin, intercellular adhesion molecule 1, and vascular cell adhesion molecule 1 (VCAM‐1) was assessed by immunohistochemistry. Results In iNOS+/+ animals with AIA, the plasma concentration of nitrite/nitrate was increased 3‐fold and iNOS expression was detected in cells of the joint. Swelling of the knee joint as well as leukocyte infiltration were enhanced in the iNOS−/− arthritic animals compared with iNOS+/+ mice with AIA. AIA‐associated leukocyte–endothelial cell interaction in synovial postcapillary venules was more pronounced in iNOS−/−, compared with iNOS+/+, arthritic mice. A strong expression of P‐selectin and VCAM‐1 was observed in the iNOS−/− arthritic mice only. Conclusion These data suggest that NO production by iNOS in vivo has antiinflammatory effects in experimental arthritis, by mediating a reduction in leukocyte adhesion and infiltration.