An alternative proposal for "Mixed randomization" by Schulz and Grimes.
OBJECTIVE
Randomization is an important part of clinical trials. Using permuted-block randomization for forcing equal group sizes potentially harms the unpredictability of treatment assignments. This can allow bias to creep into a trial. As an alternative, Schulz and Grimes suggest a "Mixed randomization" scheme which introduces more complexity to realize randomization. The objective of our research was to work out a model for randomization which is easier to handle than "Mixed randomization", with an equal level of performance in unpredictability and balance.


METHODS
We analyzed a "Mixed randomization" procedure regarding the degree of unpredictability and balancing power and compared performance using permuted-block randomization with very large block size in a worst case scenario. Our work was done by the application of Blackwell-Hodges model for evaluation of the unpredictability of treatment assignments.


RESULTS
Regarding unpredictability, performance of permuted-block randomization with block size b = 36 was very similar to that of "Mixed randomization". Regarding balancing power it was more favourable than "Mixed randomization".


CONCLUSIONS
Results of Schulz and Grimes are very important as they emphasized that mildly unequal sample sizes of therapy groups don't cause problems. But the suggested scheme of "Mixed randomization" to a large extent adds complexity and we do not believe that this proposal is very feasible. Basically, we rather recommend the use of only one restricted randomization procedure in the best way. This can be permuted-block randomization with optimum choice of a large block size.
Kundt
G
G
University of Rostock, School of Medicine, Department of Medical Informatics and Biometry, Germany. guenther.kundt@medizin.uni-rostock.de
D006801:Humans; D015233:Models, Statistical; D016017:Odds Ratio; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D012107:Research Design; D018401:Sample Size; D015983:Selection Bias; D016896:Treatment Outcome