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Clinical trials and causation: Bayesian perspectives.

K F Schaffner
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Abstract

In addition to the safety, it is essential to establish the causal efficacy of extant and new treatments, and well-designed clinical trials are thought by most to be the 'gold standard' to accomplish this. Contrary to most statisticians' and regulators' views, however, I will argue that the concept of causation involved in clinical trials is not all that clear. I discuss the manipulability approach to causation, interpreted counterfactually, which seems to fit causation as it is found in such sciences as physiology, but it has unclear relations to a concept of causation proposed by a number of epidemiologists. I characterize 'epidemiological causation' as probabilistic and formulated at a population level, and dependent on certain general criteria for causation as well as study-design considerations. I then attempt to clarify the connections between these concepts of causation and Cartwright's views on complexity and causality, a 'Bayesian' framework proposed by Rubin and further elaborated by Holland, and Glymour and his colleagues' recent directed graphical causal modelling approach.

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Bayes TheoremBiasCausalityClinical Trials as TopicEpidemiologyEthics, MedicalModels, StatisticalPhysiologyResearch DesignSafetyTreatment OutcomeJournal Article

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K F Schaffner. 1993. Clinical trials and causation: Bayesian perspectives.. https://doi.org/10.1002/sim.4780121514

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