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In this project we propose a way of computing the needed sample size, for a binary endpoint and for a binary composite endpoint, needed to detect the effect of a certain treatment considering a type I error of alpha and a statistical power of 1-beta in Randomized Clinical Trial. We propose the Sample Ratio, which is an efficiency measure to decide upon the use of a binary composite endpoint instead of a relevant endpoint. The impact of the relative overlaps on the required sample size, for the relevant endpoint and for the composite, is explored in dierent simulated scenarios. Similarly for the Sample Ratio. In addition, we compare our proposed method with the currently available Asymptotic Relative Eciency, which is specically used in the context of time-to-endpoint analyses.
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