New algorithm for treatment allocation reduced selection bias and loss of power in small trials

In clinical trials, patients become available for treatment sequentially. Especially in trials with a small number of patients, loss of power may become an important issue, if treatments are not allocated equally or if prognostic factors differ between the treatment groups. We present a new algorithm for sequential allocation of two treatments in small clinical trials, which is concerned with the reduction of both selection bias and imbalance.

With the algorithm, an element of chance is added to the treatment as allocated by minimization. The amount of chance depends on the actual amount of imbalance of treatment allocations of the patients already enrolled. The sensitivity to imbalance may be tuned. We performed trial simulations with different numbers of patients and prognostic factors, in which we quantified loss of power and selection bias.

With our method, selection bias is smaller than with minimization, and loss of power is lower than with pure randomization or treatment allocation according to a biased coin principle.

Our method combines the conflicting aims of reduction of bias by predictability and reduction of loss of power, as a result of imbalance. The method may be of use in small trials.