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Abstract
Parametric methods such as analysis of (co)variance are commonly used for the analysis of data from clinical trials. They have the advantage of providing an easily interpretable measure of treatment efficacy such as a confidence interval for treatment difference. If there are doubts about the underlying distribution of the response variable, however, a nonparametric approach may be called for. The nonparametric approaches in such settings concentrate on hypothesis testing and are not typically used for providing easily interpretable measures of treatment efficacy. For comparing two treatments, we propose using a nonparametric measure based on the likelihood of observing a better response on one treatment than the other. The bootstrap method is used to construct a confidence interval for the treatment difference.
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