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Using absolute risks to assess the risks and benefits of treatment
  1. Mitchell H Gail
  1. Correspondence to Dr Mitchell H Gail, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Room 7E-138, Rockville, MD 20850-9780, USA;gailm{at}mail.nih.gov

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Health providers and their patients are accustomed to taking risks and benefits into account when deciding whether or not to use a particular treatment. In some instances the choice is clear cut, as when the treatment has few side effects or the patient has a strong aversion to a particular health outcome. When an intervention has favourable effects on some health outcomes and unfavourable effects on others and patient preferences regarding particular outcomes are unclear, the decision becomes more difficult. Part of the difficulty is simply gathering relevant information for the decision. Most healthcare providers do not have the time to compile information on the effects of an intervention on each health outcome as well as on the absolute risk (or probability) of that outcome in the absence of intervention, which are necessary ingredients for a formal approach to decision making. Yu et al 1 have provided such information to help inform the decision of whether or not to use roflumilast to suppress exacerbations of COPD. They also address whether that decision should be ‘yes’ for some patients and ‘no’ for others.

Yu et al 1 used summary data from the US Food and Drug Administration to evaluate the risks and benefits of roflumilast to prevent exacerbations of COPD. They distinguished between moderate exacerbations and severe exacerbations, defined by hospitalisation or death. They concluded that roflumilast provided a net benefit only to patients at high risk of severe exacerbations. Here I review their methods and assumptions and indicate some opportunities for using additional data for treatment decisions.

The essential ingredients for a treatment decision are: (1) A list of the adverse health outcomes affected by the treatment. We denote the number of such health outcomes by K. For example, Yu et al studied K=11 outcomes, including COPD exacerbation and various …

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Footnotes

  • Funding This research was supported by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Division of Cancer Epidemiology and Genetics.

  • Competing interests None.

  • Provenance and peer review Not commissioned; internally peer reviewed.

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