No description available.

No description available.

No description available.

No description available.

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.

Randomization schemes for two-treatment clinical trials are studied. Theoretical expressions for the power are derived under both complete randomization and Efron’s biased coin design for normal and binary responses. The better the scheme is at balancing the numbers of patients across treatments, the higher the power is. Efron’s biased coin design is more powerful than complete randomization. Normal approximations to the powers are obtained. The power of the adjustable biased coin design is also investigated by simulation. Covariate-adaptive randomization schemes are analysed when either global or marginal balance across cells is sought. By considering a fixed-effects linear model for normal treatment responses with several covariates, an analysis of covariance t test is carried out. Its power is simulated for global and marginal balance, both in the absence and in the presence of interactions between the covariates. Global balancing covariate-adaptive schemes are more efficient when there are interactions between the covariates. Restricted randomization schemes for more than two treatments are then considered. Their asymptotic properties are provided. An adjustable biased coin design is introduced for which assignments are based on the imbalance across treatments. The finitesample properties of the imbalance under these randomization schemes are studied by simulation. Assuming normal treatment responses, the power of the test for treatment differences is also obtained and is highest for the new design. Imbalance properties of complete randomization and centre-stratified permuted block randomization for several treatments are investigated. It is assumed that the patient recruitment process follows a Poisson-gamma model. When the number of centres is large, the imbalance for both schemes is approximately multivariate normal. The power of a test for treatment differences is simulated for normal responses. The loss of power can be compensated for by a small increase in sample size.

No description available.

Thesis (M. Med. Sc.)--University of Hong Kong, 2006.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2005. / Includes bibliographical references (p. 49-50).

Thesis (M. Med. Sc.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 59-62).