Fron Chabouis Hélène, Chabouis Francis, Gillaizeau Florence, Durieux Pierre, Chatellier Gilles, Ruse N Dorin, Attal Jean-Pierre
Sorbonne Paris Cité, Faculté de chirurgie dentaire, Biomaterials department (URB2i, EA4462), Clinical Research Unit, Université Paris Descartes, 1 rue Maurice Arnoux, Montrouge, 92120, France,
Clin Oral Investig. 2014 Jan;18(1):25-34. doi: 10.1007/s00784-013-0949-8. Epub 2013 Mar 1.
Operative clinical trials are often small and open-label. Randomization is therefore very important. Stratification and minimization are two randomization options in such trials. The first aim of this study was to compare stratification and minimization in terms of predictability and balance in order to help investigators choose the most appropriate allocation method. Our second aim was to evaluate the influence of various parameters on the performance of these techniques.
The created software generated patients according to chosen trial parameters (e.g., number of important prognostic factors, number of operators or centers, etc.) and computed predictability and balance indicators for several stratification and minimization methods over a given number of simulations. Block size and proportion of random allocations could be chosen. A reference trial was chosen (50 patients, 1 prognostic factor, and 2 operators) and eight other trials derived from this reference trial were modeled. Predictability and balance indicators were calculated from 10,000 simulations per trial.
Minimization performed better with complex trials (e.g., smaller sample size, increasing number of prognostic factors, and operators); stratification imbalance increased when the number of strata increased. An inverse correlation between imbalance and predictability was observed.
A compromise between predictability and imbalance still has to be found by the investigator but our software (HERMES) gives concrete reasons for choosing between stratification and minimization; it can be downloaded free of charge.
This software will help investigators choose the appropriate randomization method in future two-arm trials.
手术临床试验通常规模较小且为开放标签。因此随机化非常重要。分层和最小化是此类试验中的两种随机化选择。本研究的首要目的是比较分层和最小化在可预测性和平衡性方面的差异，以帮助研究者选择最合适的分配方法。我们的第二个目的是评估各种参数对这些技术性能的影响。
创建的软件根据选定的试验参数（例如，重要预后因素的数量、手术者或中心的数量等）生成患者，并针对给定数量的模拟计算几种分层和最小化方法的可预测性和平衡指标。可以选择区组大小和随机分配的比例。选择了一项参考试验（50名患者、1个预后因素和2名手术者），并对从该参考试验衍生出的其他八项试验进行建模。每项试验通过10,000次模拟计算可预测性和平衡指标。
在复杂试验中（例如，样本量较小、预后因素数量增加以及手术者数量增加），最小化表现更佳；当层数增加时，分层不平衡加剧。观察到不平衡与可预测性之间呈负相关。
研究者仍需在可预测性和不平衡之间找到折衷办法，但我们的软件（HERMES）为在分层和最小化之间做出选择提供了具体依据；它可以免费下载。
该软件将帮助研究者在未来的双臂试验中选择合适的随机化方法。
