PubMed · 2321615
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G W Brown. 1990. P values.. https://doi.org/10.1001/archpedi.1990.02150280115026
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In this paper we formalize the problem of testing the one-sided equivalence in the sensitivities of two medical diagnostic tests under a matched-pair study design. We derive conditional and unconditional sample size formulae which are decreasing functions of the probability of being diagnosed by both tests. We calculate upper boundary and midpoint sample sizes. Results of a Monte Carlo simulation study that compares the proposed sample size formulae with that of Lachenbruch suggest that our midpoint conditional sample size is the best choice to obtain the desired power for the type of equivalence studies discussed in the paper.
To prove the hypothesis that a new treatment is as effective as a standard one, a possibility is to test the one-sided null hypothesis of a clear inferiority of the new treatment against the alternative hypothesis that, if at all, it is only negligibly inferior. Such a problem is of clinical relevance if, for instance, a new treatment with an effectiveness which is comparable to that of the standard one would be preferred if it is less toxic. If the difference between the two treatments is measured by the difference of failure rates, approximate statistical tests and sample size formulae have to be used. This paper reports the results of an extensive empirical investigation comparing the well known calculations proposed by Blackwelder, Rodary, ComNougue and Tournade, which propose a sample size formula for the test of Dunnett and Gent, and Farrington and Manning. The investigation was conducted in order to allow more comprehensive conclusions than those which may be drawn from the limited examples given by the last authors. For the usual settings in clinical trials, the formulae of Farrington and Manning are recommended. However, there are combinations of statistical parameters for which they are not preferred.