Gynaecological Clinic Taastrup, Taastrup Hovedgade 129 D, DK-2630 Taastrup, Denmark.
Contemp Clin Trials. 2010 Mar;31(2):147-50. doi: 10.1016/j.cct.2009.12.001. Epub 2010 Jan 6.
Minimization (M) is the most popular algorithm for balancing large numbers of subject variables in treatment groups of small clinical trials. However, its use has been limited because of its complexity, vulnerability to selection bias and lack of a generally accepted method for statistical analysis of the data. Rank-Minimization (RM) is a promising new algorithm. It is less complex since it does not require unique programming for each clinical trial to convert continuous to categorical variables. In this study RM is compared to M for balance of variables and vulnerability to selection bias in 1000 simulated trials using 200 subjects with 15 continuous variables. With RM there were no instances of significant imbalance to cause rejection of the null hypothesis, i.e. a Student's t> or =2, although it occurred in 0.4% of the 15000 tests for M. For moderate imbalance, i.e. 1< or = t < 2, the figures were 3% (RM) and 12% (M). The probability of guessing the next assignment was 0.636 (RM) and 0.683 (M). The smaller figure is superior to that of restricted randomization in blocks of five per treatment group. Improvement in balance, a decrease in vulnerability to selection bias and ease of application along with improvements in the statistical analysis should result in the general acceptance of RM for assigning subjects to treatment groups in clinical trials.
最小化（M）是平衡小临床试验治疗组中大量受试者变量最常用的算法。然而，由于其复杂性、易受选择偏差的影响以及缺乏普遍接受的数据分析方法，其应用受到限制。秩最小化（RM）是一种很有前途的新算法。它不那么复杂，因为它不需要为每个临床试验进行独特的编程，将连续变量转换为分类变量。在这项研究中，使用 200 名受试者的 15 个连续变量，对 1000 次模拟试验中的变量平衡和对选择偏差的脆弱性，将 RM 与 M 进行了比较。在 RM 中，没有出现导致拒绝零假设的显著不平衡的情况，即学生 t>或=2，尽管在 M 的 15000 次测试中有 0.4%的情况出现。对于中度不平衡，即 1<或=t<2，其比例分别为 3%（RM）和 12%（M）。猜测下一个分配的概率为 0.636（RM）和 0.683（M）。较小的数字优于每个治疗组 5 个块的限制随机化。平衡的改善、选择偏差脆弱性的降低、应用的简便性以及统计分析的改进，应该会导致 RM 被普遍接受，用于将受试者分配到临床试验的治疗组中。
