title: A re-randomisation design for clinical trials.
abstract: BACKGROUND
  Recruitment to clinical trials is often problematic, with many trials failing to recruit to their target sample size. As a result, patient care may be based on suboptimal evidence from underpowered trials or non-randomised studies.
  
  
  METHODS
  For many conditions patients will require treatment on several occasions, for example, to treat symptoms of an underlying chronic condition (such as migraines, where treatment is required each time a new episode occurs), or until they achieve treatment success (such as fertility, where patients undergo treatment on multiple occasions until they become pregnant). We describe a re-randomisation design for these scenarios, which allows each patient to be independently randomised on multiple occasions. We discuss the circumstances in which this design can be used.
  
  
  RESULTS
  The re-randomisation design will give asymptotically unbiased estimates of treatment effect and correct type I error rates under the following conditions: (a) patients are only re-randomised after the follow-up period from their previous randomisation is complete; (b) randomisations for the same patient are performed independently; and (c) the treatment effect is constant across all randomisations. Provided the analysis accounts for correlation between observations from the same patient, this design will typically have higher power than a parallel group trial with an equivalent number of observations.
  
  
  CONCLUSIONS
  If used appropriately, the re-randomisation design can increase the recruitment rate for clinical trials while still providing an unbiased estimate of treatment effect and correct type I error rates. In many situations, it can increase the power compared to a parallel group design with an equivalent number of observations.
authors.affiliation: Pragmatic Clinical Trials Unit, Queen Mary University of London, London, E1 2AB, UK. b.kahan@qmul.ac.uk.
authors.forename: Brennan C
authors.initials: BC
authors.lastname: Kahan
authors.affiliation: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia. andrew.forbes@monash.edu.
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Forbes
authors.affiliation: Comprehensive Clinical Trials Unit, University College London, London, WC1E 6BT, UK. caroline.dore@ucl.ac.uk.
authors.forename: Caroline J
authors.initials: CJ
authors.lastname: Doré
authors.affiliation: Hub for Trials Methodology Research, MRC Clinical Trials Unit at UCL, London, WC2B 6NH, UK. tim.morris@ucl.ac.uk.
authors.forename: Tim P
authors.initials: TP
authors.lastname: Morris
mesh_terms: D006801:Humans; D018579:Patient Selection; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D018401:Sample Size; D016896:Treatment Outcome