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A problem for achieving informed choice

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Abstract

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.

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Notes

  1. Two immediate questions: what constraints on an individual’s therapeutic choice are appropriate? And, when is “all else equal”? It is clear, for example, that expert advice, best evidence, and the availability of resources play a legitimate role in constraining therapeutic choice. It is also clear that everything is not equal when an individual’s rationality is in question. Then the question then becomes: what happens to the clinician’s obligation to inform choice? For a comparison of informed choice and shared decision making in the context of general practice, see [1]. For one example of a discussion on informed consent when rationality is in question, see [2].

  2. For a general discussion, see [3]; for a discussion in relation to health care, [4].

  3. Thanks to an anonymous reviewer for emphasising this point. A useful example, provided by the reviewer, is of a drug that can reduce fertility. An individual wanting contraceptive cover will perceive this outcome as beneficial; an individual wanting to conceive, as an adverse effect; and an individual indifferent to the effect of the drug on fertility—for whatever reason—will be uninterested in the probability of the effect.

  4. An example of eliciting “utilities” is provided in [11]. For an example of risk communication, see [9].

  5. I put “accept” in scare quotes to acknowledge that classical statistics never fully accepts a hypothesis that has passed a test. This is in line with Popperian philosophy of science. Classical statistics provides methods for deciding when a hypothesis, usually a “null hypothesis,” can be rejected. It is when the conditions for rejection of the null hypothesis are appropriately met, that the alternative hypothesis can be provisionally “accepted.”

  6. This also assumes that the sampling distribution is monotonic, and assumes that any measurement error is small and distributed randomly.

  7. For a review of Bayesian analysis in clinical trials, see [18].

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Acknowledgements

Thanks to Jason Grossman and Mark Colyvan for helpful discussion and comments on earlier drafts. I would also like to thank two anonymous referees of Theoretical Medicine and Bioethics.

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  1. Philosophy Department, Main Quad, University of Sydney, Sydney, 2006, Australia

    Adam La Caze

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  1. Adam La Caze
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Correspondence to Adam La Caze.

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La Caze, A. A problem for achieving informed choice. Theor Med Bioeth 29, 255–265 (2008). https://doi.org/10.1007/s11017-008-9069-x

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