title: Introduction to a generalized method for adaptive randomization in trials.
abstract: BACKGROUND
  Ideally clinical trials should use some form of randomization for allocating participants to the treatment groups under trial. As an integral part of the process of assessing the effectiveness of these treatment groups, randomization performed well can reduce, if not eliminate, some forms of bias that can be evident in non-randomized trials. Given the vast set of possible randomization methods to choose from we demonstrate a method that incorporates many of the advantages of these other methods.
  
  
  METHODS
  A step-by-step introduction of how to use the adaptive randomization algorithm for conducting a clinical trial is given.
  
  
  RESULTS
  The implications, effects and capabilities of using the adaptive randomization algorithm are fully demonstrated and explained using simulated data and examples from actual trials.
  
  
  CONCLUSIONS
  This paper provides an introduction to a dynamic type of treatment allocation, which fulfills the CONSORT requirements of participants being randomly allocated whilst maintaining a level of control of the balances overall, within the stratification variables and within the strata simultaneously. Maintaining control of the imbalances within the groups is vital particularly if interim analyses are planned.
  
  
  BACKGROUND
  Current controlled trials ISRCTN17551624, ISRCTN37558856, ISRCTN97185214.
authors: NWORTH Bangor Clinical Trials Unit, Institute of Medical & Social Care Research, Y Wern, Holyhead Road, George Site, Bangor University, Bangor, LL57 2PZ, GwyneddUK. z.hoare@bangor.ac.uk
authors: Zoë S J
authors: ZS
authors: Hoare
authors: Christopher J
authors: CJ
authors: Whitaker
authors: Rhiannon
authors: R
authors: Whitaker
mesh_terms: D000465:Algorithms; D015982:Bias; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D012737:Sex Factors