David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jonathan Jenkins Ichikawa
Jack Alan Reynolds
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Theoretical Medicine and Bioethics 29 (4):255-265 (2008)
Most agree that, if all else is equal, patients should be provided with enough information about proposed medical therapies to allow them to make an informed decision about what, if anything, they wish to receive. This is the principle of informed choice; it is closely related to the notion of informed consent. Contemporary clinical trials are analysed according to classical statistics. This paper puts forward the argument that classical statistics does not provide the right sort of information for informing choice. The notion of probability used by classical statistics is complex and difficult to communicate. Therapeutic decisions are best informed by statistical approaches that assign probabilities to hypotheses about the benefits and harms of therapies. Bayesian approaches to statistical inference provide such probabilities.
|Keywords||Classical statistics Bayesian statistics Informed choice Informed consent Randomised controlled trials 1103 Clinical Sciences|
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References found in this work BETA
Daniel Kahneman, Paul Slovic & Amos Tversky (eds.) (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
J. Savulescu & R. W. Momeyer (1997). Should Informed Consent Be Based on Rational Beliefs? Journal of Medical Ethics 23 (5):282-288.
M. Kottow (2004). The Battering of Informed Consent. Journal of Medical Ethics 30 (6):565-569.
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