Key points are not available for this paper at this time.
Abstract Power analysis for sample size calculation (power calculation) plays an important role in clinical research to guarantee that we have sufficient power for detecting a clinically meaningful difference (treatment effect) at a pre-specified level of significance. In practice, however, there may be little or no information regarding the test treatment under study available. In this case, it is suggested that power calculation for detecting an anticipated effect size adjusted for standard deviation be performed. In practice, power calculation based on effect size is commonly considered for a quick assessment of sample size requirement. It reduces a two-parameter problem into a single parameter problem by taking both mean response and variability into consideration. However, this approach has been criticized that the resultant sample size may not guarantee that final clinical results are reproducible if the variability is large. In addition, for a fixed effect size, study endpoints of different data types cannot translate one another in terms of clinically meaningful differences. This article meant to provide a comprehensive summarization of the relationship between power calculation based on effect size and based on treatment effect in terms of different study endpoints of different data types.
Zhang et al. (Tue,) studied this question.