title: Comparing two treatments by decision theory.
abstract: Decision theory is applied to the general problem of comparing two treatments in an experiment with subjects assigned to the treatments at random. The inferential agenda covers collection of evidence about superiority, non-inferiority and average bioequivalence of the treatments. The proposed approach requires defining the terms 'small' and 'large' to qualify the magnitude of the treatment effect and specifying the losses (or loss functions) that quantify the consequences of the incorrect conclusions. We argue that any analysis that ignores these two inputs is deficient, and so is any ad hoc way of taking them into account. Sample size calculation for studies intended to be analysed by this approach is also discussed. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Medicine, Imperial College, London, UK. sntlnick@sntl.co.uk.
authors.forename: Nicholas T
authors.identifier: 0000-0003-4129-9726
authors.initials: NT
authors.lastname: Longford
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data*; D003662:Decision Theory; D006801:Humans; D018401:Sample Size; D013810:Therapeutic Equivalency; D016896:Treatment Outcome
keywords: clinical trial; hypothesis test; loss function; treatment effect; verdict