I'm currently setting up an experiment where we might want to do multiple comparisons (i.e. comparing several treatments with a control at the same time). It is pretty straightforward to calculate the needed sample-size using the Bonferroni correction. However, the Bonferroni method is pretty conservative, and I'm worried that we're wasting time or resources getting more samples than we actually need.
Are there ways to calculate needed sample-size for other correction methods, such as Benjamini-Hochberg, Holm-Bonferroni, Westfall-Young correction?
Or, in your experience, are you likely to see any significant decrease (more than 5%) in sample-size using any of these other methods at all?
The test in question is a simple comparison of treatment effects on a categorical outcome variable, with expected mean at 50%.