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Statistical pitfalls of personalized medicine

Misleading terminology and arbitrary divisions stymie drug trials and can give false hope about the potential of tailoring drugs to individuals, warns Stephen Senn.
  1. Stephen Senn

    1. Stephen Senn was formerly head of the Competence Center for Methodology and Statistics at the Luxembourg Institute of Health.

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Illustration by David Parkins

Personalized medicine aims to match individuals with the therapy that is best suited to them and their condition. Advocates proclaim the potential of this approach to improve treatment outcomes by pointing to statistics about how most drugs — for conditions ranging from arthritis to heartburn — do not work for most people1. That might or might not be true, but the statistics are being misinterpreted. There is no reason to think that a drug that shows itself to be marginally effective in a general population is simply in want of an appropriate subpopulation in which it will perform spectacularly.

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Nature 563, 619-621 (2018)

doi: https://doi.org/10.1038/d41586-018-07535-2

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