Dupin-Spriet Thérèse, Fermanian Jacques, Spriet Alain
Laboratoire de pharmacologie, de pharmacocinétique et de pharmacie clinique, Faculté de Pharmacie, Lille, France.
J Clin Epidemiol. 2005 Dec;58(12):1269-76. doi: 10.1016/j.jclinepi.2005.04.008.
A selection of patients for a controlled clinical trial may be biased because of prior knowledge of the treatment. With randomized blocks of known or guessed lengths, some allocations can be predicted with certainty. Previously described methods determine the proportion of predictable cases for blocks of equal lengths. It may be useful to make a calculation for unequal blocks as well to find a method that reduces this predictability.
Quantification methods are developed for series of two and three unequal blocks, using the probability of identifying a long block when it comes before a short one if it starts with a sequence incompatible with the content of a short block. Results are compared with the recently described maximal allocation procedure.
Predictability is not always reduced by unequal blocks and is even worse in some cases, compared to equal blocks. Predictability is not necessarily decreased with the maximal allocation procedure.
Before choosing an allocation method, it is important to quantify the predictability of possible options to reduce selection bias. Several practical recommendations are formulated for choosing methods, taking this risk of bias into account.
由于对治疗方法的先验了解,在选择患者进行对照临床试验时可能会产生偏差。对于已知或猜测长度的随机区组,某些分配可以确定地预测。先前描述的方法确定了等长区组中可预测病例的比例。计算不等长区组的情况并找到一种降低这种可预测性的方法可能会很有用。
针对两个和三个不等长区组的序列开发了量化方法,利用当一个长区组在一个短区组之前出现且其起始序列与短区组内容不兼容时识别长区组的概率。将结果与最近描述的最大分配程序进行比较。
与等长区组相比,不等长区组并不总是能降低可预测性,在某些情况下甚至更糟。最大分配程序不一定能降低可预测性。
在选择分配方法之前,量化可能选项的可预测性以减少选择偏差很重要。考虑到这种偏差风险,针对选择方法提出了一些实用建议。