title: Causal assessment of pharmaceutical treatments: why standards of evidence should not be the same for benefits and harms?
abstract: It is increasingly acknowledged both among epidemiologists and regulators that the assessment of pharmaceutical harm requires specific methodological approaches that cannot simply duplicate those developed for testing efficacy. However, this intuition lacks sound epistemic bases and delivers ad hoc advice. This paper explains why the same methods of scientific inference do not fare equally well for efficacy and safety assessment by tracing them back to their epistemic foundations. To illustrate this, Cartwright's distinction into clinching and vouching methods is adopted and a series of reasons is provided for preferring the latter to the former: (1) the need to take into account all available knowledge and integrate it with incoming data; (2) the awareness that a latent unknown risk may always change the safety profile of a given drug (precautionary principle); (3) cumulative learning over time; (4) requirement of probabilistic causal assessment to allow decision under uncertainty; (5) impartiality; and (6) limited and local information provided by randomised controlled trials. Subsequently, the clinchers/vouchers distinction is applied to a case study concerning the debated causal association between paracetamol and asthma. This study illustrates the tension between implicit epistemologies adopted in evaluating evidence and causality; furthermore, it also shows that discounting causal evidence may be a result of unacknowledged low priors or lack of valid alternative options. We conclude with a presentation of the changing landscape in pharmacology and the trend towards an increased use of Bayesian tools for assessment of harms.
authors.affiliation: University of Camerino, Camerino, MC, Italy, barbara.osimani@unicam.it.
authors.forename: Barbara
authors.initials: B
authors.lastname: Osimani
authors.forename: Fiorenzo
authors.initials: F
authors.lastname: Mignini
mesh_terms: D000082:Acetaminophen / Q000009:adverse effects; D001249:Asthma / Q000139:chemically induced; D001499:Bayes Theorem; D015984:Causality; D064420:Drug-Related Side Effects and Adverse Reactions / Q000175:diagnosis* / Q000209:etiology; D006801:Humans; D011336:Probability; D012107:Research Design; D012306:Risk; D018570:Risk Assessment / Q000379:methods*
chemical_list: D000082:Acetaminophen