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The role of minimisation in treatment allocation for clinical trials

Simple randomisation is the easiest method for allocating participants to treatment groups in clinical trials. In the long run it balances all features of participants across the groups but may not be suitable for small to medium sized trials. If important prognostic factors are identified at the design stage then stratified randomisation or minimisation can help to balance these features. Aim: To examine the relative benefits of different randomisation algorithms and determine guidelines for which randomisation design is advisable for a given trial. For a trial of known size with a specified number of important prognostic factors, and levels within these, it will be possible to identify the most appropriate randomisation technique for that trial. Methods: A review of methods of randomisation was first conducted followed by a survey of trialists into the current use of randomisation methods in clinical trials. Using simulations the following comparisons were made; simple randomisation compared with minimisation, whether to stratify or minimise by centre and predictability versus balance when using minimisation. The recommendations resulting from the simulations were used to design a prototype generic randomisation program. Results: The review and the survey both highlighted the probability of imbalance using simple randomisation. Minimisation was seen to be superior in producing balanced groups but the method was criticised for being more complex and unpredictable. The simulations showed that several factors influence imbalance including size of trial, the number of prognostic factors and the number of categories within these. Optimal algorithms for maintaining balance while reducing predictability were presented for varying trial parameters. Conclusions: Minimisation is a suitable method of randomisation for most clinical trials. Several strategies can be employed to address the conflicting issues of predictability and imbalance without resorting to complex mathematical algorithms.

Identiferoai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:542643
Date January 2011
CreatorsMcPherson, Gladys
PublisherUniversity of Aberdeen
Source SetsEthos UK
Detected LanguageEnglish
TypeElectronic Thesis or Dissertation
Sourcehttp://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=167718

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