Search

Abstract: Objectives: In randomized controlled trials with many potential prognostic factors, serious imbalance among treatment groups regarding these factors can occur. Minimization methods can improve balance but increase the possibility of selection bias. We described and evaluated the performance of a new method of treatment allocation, called studywise minimization, that can avoid imbalance by chance and reduce selection bias. Study Design and Setting: The studywise minimization algorithm consists of three steps: (1) calculate the imbalance for all possible allocations, (2) list all allocations with minimum imbalance, and (3) randomly select one of the allocations with minimum imbalance. We carried out a simulation study to compare the performance of studywise minimization with three other allocation methods: randomization, biased-coin minimization, and deterministic minimization. Performance was measured, calculating maximal and average imbalance as a percentage of the group size. Results: Independent of trial size and number of prognostic factors, the risk of serious imbalance was the highest in randomization and absent in studywise minimization. The largest differences among the allocation methods regarding the risk of imbalance were found in small trials. Conclusion: Studywise minimization is particularly useful in small trials, where it eliminates the risk of serious imbalances without generating the occurrence of selection bias. [ABSTRACT FROM AUTHOR]

Abstract: Objective: In some trials, the intervention is delivered to individuals in groups, for example, groups that exercise together. The group structure of such trials has to be taken into consideration in the analysis and has an impact on the power of the trial. Our aim was to provide optimal methods for the design and analysis of such trials. Study Design and Setting: We described various treatment allocation methods and presented a new allocation algorithm: optimal batchwise minimization (OBM). We carried out a simulation study to evaluate the performance of unrestricted randomization, stratification, permuted block randomization, deterministic minimization, and OBM. Furthermore, we described appropriate analysis methods and derived a formula to calculate the study size. Results: Stratification, deterministic minimization, and OBM had considerably less risk of imbalance than unrestricted randomization and permuted block randomization. Furthermore, OBM led to unpredictable treatment allocation. The sample size calculation and the analysis of the study must be based on a multilevel model that takes the group structure of the trial into account. Conclusion: Trials evaluating interventions that are carried out in subsequent groups require adapted treatment allocation, power calculation, and analysis methods. From the perspective of obtaining overall balance, we conclude that minimization is the method of choice. When the number of prognostic factors is low, stratification is an excellent alternative. OBM leads to better balance within the batches, but it is more complicated. It is probably most worthwhile in trials with many prognostic factors. From the perspective of predictability, a treatment allocation method, such as OBM, that allocates several subjects at the same time, is superior to other methods because it leads to the lowest possible predictability. [Copyright &y& Elsevier]