I have pre- and post-treatment continuous data for a large number of variables that I am analyzing for treatment effect. Normally I would obtain the P values and then adjust them for multiple testing with a method such as Benajmini-Hochberg.
However, the statistical test that I am using is somewhat computationally intensive. To reduce this load, I am thinking of first filtering the data by removing variables for which the average effect size is less than 2-fold in either direction; i.e.,
Will such filtering of data by effect size violate some assumption of P value adjustment methods? It seems that the filtering is likely to enrich for variables for which a truly positive treatment effect exists.