Studywise minimization: a treatment allocation method that improves balance among treatment groups and makes allocation unpredictable

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.