title: Connections between permutation and t-tests: relevance to adaptive methods.
abstract: A permutation test assigns a p-value by conditioning on the data and treating the different possible treatment assignments as random. The fact that the conditional type I error rate given the data is controlled at level α ensures validity of the test even if certain adaptations are made. We show the connection between permutation and t-tests, and use this connection to explain why certain adaptations are valid in a t-test setting as well. We illustrate this with an example of blinded sample size recalculation.
authors.affiliation: National Institute of Allergy and Infectious Diseases, Bethesda, Maryland.
authors.forename: Michael
authors.initials: M
authors.lastname: Proschan
authors.forename: Ekkehard
authors.initials: E
authors.lastname: Glimm
authors.forename: Martin
authors.initials: M
authors.lastname: Posch
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric
keywords: adaptive methods in clinical trials; asymptotic distribution; blinded sample size recalculation; complete sufficient statistic; p-value combination functions; permutation tests