1,720,974 research outputs found
Scientific Evidence and the Law: An Objective Bayesian Formalisation of the Precautionary Principle in Pharmaceutical Regulation
Full text linkThe paper considers the legal tools that have been developed in German pharmaceutical regulation as a result of the precautionary attitude inaugurated by the Contergan decision (1970). These tools are (i) the notion of "well-founded suspicion", which attenuates the requirements for safety intervention by relaxing the requirement of a proved causal connection between danger and source, and the introduction of (ii) the reversal of proof burden in liability norms. The paper focuses on the first and proposes seeing the precautionary principle as an instance of the requirement that one should maximise expected utility. In order to maximise expected utility certain probabilities are required and it is argued that objective Bayesianism offers the most plausible means to determine the optimal decision in cases where evidence supports diverging choices
Barbara Osimani’s contribution to the Discussion of ‘Testing by betting: A strategy for statistical and scientific communication’ by Glenn Shafer
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Varieties of Error and Varieties of Evidence in Scientific Inference
Full text linkAccording to the variety of evidence thesis items of evidence from independent lines of investigation are more confirmatory, ceteris paribus, than, for example, replications of analogous studies. This thesis is known to fail (Bovens and Hartmann; Claveau). However, the results obtained by Bovens and Hartmann only concern instruments whose evidence is either fully random or perfectly reliable; instead, for Claveau, unreliability ismodelled as deterministic bias. In both cases, the unreliable instrument delivers totally irrelevant information.We present a model that formalizes both reliability and unreliability differently. Our instruments either are reliable, but affected by random error, or are biased but not deterministically so. Bovens and Hartmann’s results are counter-intuitive in that in their model a long series of consistent reports from the same instrument does not raise suspicion of ‘too-good-to-be-true’ evidence. This happens precisely because they contemplate neither the role of systematic bias, nor unavoidable random error of reliable instruments. In our model, the variety of evidence thesis fails as well, but the area of failure is considerably smaller than for Bovens and Hartmann and Claveau, and holds for (the majority of ) realistic cases (that is, where biased instruments are very biased). The essential mechanism that triggers variety of evidence thesis failure is the rate of false to true positives for the two kinds of instruments. Our emphasis is on modelling beliefs about sources of knowledge and their role in hypothesis confirmation in interaction with dimensions of evidence, such as variety and consistency
New insights in computational methods for pharmacovigilance: e-synthesis, a bayesian framework for causal assessment
Full text linkToday’s surge of big data coming from multiple sources is raising the stakes that pharmacovigilance has to win, making evidence synthesis a more and more robust approach in the field. In this scenario, many scholars believe that new computational methods derived from data mining will effectively enhance the detection of early warning signals for adverse drug reactions, solving the gauntlets that post-marketing surveillance requires. This article highlights the need for a philosophical approach in order to fully realize a pharmacovigilance 2.0 revolution. A state of the art on evidence synthesis is presented, followed by the illustration of E-Synthesis, a Bayesian framework for causal assessment. Computational results regarding dose-response evidence are shown at the end of this article
E-synthesis: A Bayesian framework for causal assessment in pharmacosurveillance
Full text linkBackground: 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
Real and Virtual Clinical Trials: A Formal Analysis
Full text linkSystems biology is an interdisciplinary approach to complex biological problems through modelling, simulation, and systems-level analysis, which is increasingly establishing itself as an alternative and complementary source of knowledge to standards laboratory, clinical and epidemiologic studies in medicine. It has been proposed that such computer simulation and computer-aided modelling techniques could be employed in the setting of clinical testing, in order to support the planning of clinical trials, refine their conduct and reduce the possibility of their failure. According to this view, patient-specific computer models should be used to generate simulated populations, on which new biomedical products can be safely tested. The Avicenna Alliance refers to this methodology as In Silico Clinical Trial (ISCT). In their recently published Roadmap (Viceconti et al., 2016), the Avicenna alliance produced an in-depth examination of the scientific, technological, and societal obstacles that have to be overcome in order to establish a role for the ISCT in medical research. With the present paper we provide an analysis of ISCTs epistemological status, in particular with respect to the gold standard instrument of clinical investigation: Randomized Controlled Trials. We draw on Cartwright's analysis (2011) of RCTs as a basis for a formal analysis of their epistemic value and as a benchmark for investigating ISCTs. Britton et al.'s study (Britton et al., 2013) on the impact of ion current variability on cardiac electrophysiology is used for illustrative purposes
The Cochrane Case: An Epistemic Analysis on Decision-Making and Trust in Science in the Age of Information
Full text linkIn this study we analyze a recent controversy within the biomedical world, concerning the evaluation of safety of certain vaccines. This specific struggle took place among experts: the Danish epidemiologist Peter Gøtzsche on one side and a respected scientific institution, the Cochrane, on the other. However, given its relevance, the consequences of such a conflict invest a much larger spectrum of actors, last but not least the public itself. Our work is aimed at dissecting a specific aspect happening in this complex scenario: strategy. In other words, we want to highlight the value and the impact of strategic decisions when complex issues, as those analyzed, are at stake. In order to address this we have decided to adopt a game-theoretic approach. Our work will be structured as it follows. First, we will introduce the controversy and the two main actors: Peter Gøtzsche and the Cochrane. Second, we will explain why this controversy is important and its value beyond its academic relevance. Third, we will frame the controversy as a game and will provide several models representing different situations, also furnishing an analysis of these distinct scenarios. In the end we will argue why such game-theoretic approach can be useful in dissecting this type of issues
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