Hofmeijer J, Anema P C, van der Tweel I
Department of Neurology, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands.
J Clin Epidemiol. 2008 Feb;61(2):119-24. doi: 10.1016/j.jclinepi.2007.04.002. Epub 2007 Aug 23.
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.
在临床试验中，患者是依次进入治疗阶段的。尤其是在患者数量较少的试验中，如果治疗分配不均或各治疗组之间的预后因素不同，效能降低可能会成为一个重要问题。我们提出了一种用于小型临床试验中两种治疗方法顺序分配的新算法，该算法旨在减少选择偏倚和不均衡性。
通过该算法，在最小化分配的治疗方案中加入了随机因素。随机因素的量取决于已入组患者治疗分配的实际不均衡程度。对不均衡的敏感度可以进行调整。我们对不同数量的患者和预后因素进行了试验模拟，在模拟中对效能降低和选择偏倚进行了量化。
使用我们的方法，选择偏倚比最小化方法小，效能降低比单纯随机化或根据偏倚硬币原则进行治疗分配更低。
我们的方法将通过可预测性减少偏倚和因不均衡性导致的效能降低这两个相互冲突的目标结合起来。该方法可能在小型试验中有用。
