Using absolute risks to assess the risks and benefits of treatment.

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 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 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 gastrointestinal, neurological and psychological outcomes (see their table 2). (2) Estimates of the absolute risks (probabilities) of each adverse outcome in the absence and presence of intervention. We use the symbol P0k to denote the probability of outcome k in the absence of treatment and P1k to denote the probability of the outcome in the presence of treatment. (3) The loss (or weight or severity), wk, associated with each adverse outcome. The treatment has a beneficial effect on outcome k if ðP0k P1kÞ . 0 because treatment reduces the probability of the adverse event. If ðP0k P1kÞ , 0, the treatment is harmful for outcome k. A criterion for recommending treatment is: treat if XK