more appropriate. The confidence interval also helps us interpret "negative" studies in which the authors have concluded that the experimental treatment is no better than control therapy. All we need do is look at the upper boundary of the confidence interval. If the RRR at this upper boundary would, if true, be clinically important, the study has failed to exclude an important treatment effect. In the first example we presented in this section, the upper boundary of the confidence interval was a RRR of 59%. Clearly, if this represented the truth, the benefit of the treatment would be substantial, and we would conclude that, although the investigators had failed to prove that experimental treatment was better than placebo, they also had failed to prove that it was not; they could not exclude a large, positive treatment effect. Once again the clinician must bear in mind the privoso about the arbitrariness of the choice of 95% boundaries for the confidence interval. A reasonable alternative, a 90% confidence interval, would be somewhat narrower. What can the clinician do if the confidence interval around the RRR is not reported in the article? There are three approaches, and we present them in order of increasing complexity. The easiest approach is to examine the p-value. If the p-value is exactly 0.05, then the lower bound of the 95% confidence limit for the RRR has to lie exactly at zero (a relative risk of 1), and you cannot exclude the possibility that the treatment has no