title: The statistical analysis of a clinical trial when a protocol amendment changed the inclusion criteria.
abstract: BACKGROUND
  Sometimes, protocol amendments that change the inclusion and exclusion criteria are required in clinical trials. Then, the patient populations before and after the amendment may differ.
  
  
  METHODS
  We propose to perform separate statistical tests for the different phases, i.e. for the patients recruited before and after the amendment, and to combine the tests using Fisher's combination test. After a significant combination test a multiple testing procedure can be applied to identify the phase(s) to which a proof of efficacy refers. We assume that the amendment(s) are not based on any type of unblinded data. The proposed method is investigated within a simulation study.
  
  
  RESULTS
  The proposed combination approach is superior to the 'naïve' strategy to ignore the differences between the phases and pooling the data to perform just one statistical test. This superiority disappears when there are hardly any differences between the two phases.
  
  
  CONCLUSIONS
  When one or more protocol amendments change the inclusion and exclusion criteria, one should realize that the populations may differ. In this case, separate tests for the different phases together with a combination test are a powerful method that can be applied in a variety of settings. The (first) amendment should specify the combination test to be applied in order to combine the different phases.
authors.affiliation: Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Hufelandstr, 55, 45122 Essen, Germany. christian.loesch@uk-essen.de
authors.forename: Christian
authors.initials: C
authors.lastname: Lösch
authors.forename: Markus
authors.initials: M
authors.lastname: Neuhäuser
mesh_terms: D002985:Clinical Protocols; D002986:Clinical Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D018579:Patient Selection; D012107:Research Design