@article{ref1,
title="Clinical trials and causation: Bayesian perspectives",
journal="Statistics in Medicine",
year="1993",
author="Schaffner, K. F.",
volume="12",
number="15-16",
pages="1477-94; discussion 1495",
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.<p /> <p>Language: en</p>",
language="en",
issn="0277-6715",
doi="",
url="http://dx.doi.org/"
}