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Trials  2007 

Responder analyses and the assessment of a clinically relevant treatment effect

DOI: 10.1186/1745-6215-8-31

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Abstract:

A clinical endpoint used to determine the efficacy of an experimental treatment can be measured on a variety of scales, including a continuous scale, an ordinal scale, or a binary scale. One important goal common to many clinical trials is determining whether or not the effect of the experimental treatment is significantly better than that of the control treatment, and this goal can be achieved using measurements based on any of these scales. However, it is not only important to assess statistical significance, but also to assess the clinical relevance of the effect, and the assessment of clinical relevance has received much less attention in the statistical literature. The purpose of this paper is to discuss some of the issues associated with the assessment of clinical relevance, focusing in particular on an approach known as the "responder analysis" that involves dichotomizing a continuous or ordinal variable into a binary variable.One potential approach to assess clinical relevance is to define a clinically relevant effect, and test the null hypothesis that the true effect is of this size or less versus the hypothesis that the true effect is greater than the clinically relevant effect. For example, suppose that the clinical endpoint is a continuous variable, X, such that larger values represent better efficacy, and that there is interest in the mean difference in this endpoint, μ, between the experimental treatment and the control. Note that X could represent a measurement taken at the conclusion of the trial or a change in that measurement from its baseline value. The typical null hypothesis (assuming one-sided testing) is that of no difference, or μ ≤ 0, versus the alternative hypothesis μ > 0. However, if one were to define a minimum clinically important difference, μ0, then one could test the null hypothesis μ ≤ μ0 versus the alternative hypothesis μ > μ0. This hypothesis is sometimes referred to as "super superiority," and a statistically significant result

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