title: Mid-course sample size modification in clinical trials based on the observed treatment effect.
abstract: It is not uncommon to set the sample size in a clinical trial to attain specified power at a value for the treatment effect deemed likely by the experimenters, even though a smaller treatment effect would still be clinically important. Recent papers have addressed the situation where such a study produces only weak evidence of a positive treatment effect at an interim stage and the organizers wish to modify the design in order to increase the power to detect a smaller treatment effect than originally expected. Raising the power at a small treatment effect usually leads to considerably higher power than was first specified at the original alternative. Several authors have proposed methods which are not based on sufficient statistics of the data after the adaptive redesign of the trial. We discuss these proposals and show in an example how the same objectives can be met while maintaining the sufficiency principle, as long as the eventuality that the treatment effect may be small is considered at the design stage. The group sequential designs we suggest are quite standard in many ways but unusual in that they place emphasis on reducing the expected sample size at a parameter value under which extremely high power is to be achieved. Comparisons of power and expected sample size show that our proposed methods can out-perform L. Fisher's 'variance spending' procedure. Although the flexibility to redesign an experiment in mid-course may be appealing, the cost in terms of the number of observations needed to correct an initial design may be substantial.
authors.affiliation: Department of Mathematical Sciences, University of Bath, UK. cj@maths.bath.ac.uk
authors.forename: Christopher
authors.initials: C
authors.lastname: Jennison
authors.forename: Bruce W
authors.initials: BW
authors.lastname: Turnbull
mesh_terms: D017326:Clinical Trials, Phase III as Topic / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D012107:Research Design; D018401:Sample Size; D016896:Treatment Outcome