Holding onto power: why confidence intervals are not (usually) the best basis for sample size calculations

Results With a little simplification, the sample size calculations for the traditional power-based approach, for equivalence studies, and following the new proposal can be shown to be much the same. The single fundamental difference is that the new proposal does not include a multiplier to increase the statistical power beyond 50% (i.e. only a 50:50 chance of detecting a true treatment effect of clinically important magnitude). The attempt to avoid having to define a minimum clinically important difference on a predefined primary outcome is wholly unsuccessful. The calculation of confidence interval width must be based on a particular outcome measure, still requires the size of an unimportant difference to be defined if the confidence interval is to exclude it, and additionally requires a likely true effect of treatment to be defined about which the confidence interval will be centred.