PROPERTIES OF SIMPLE RANDOMIZATION IN CLINICAL-TRIALS
PROPERTIES OF SIMPLE RANDOMIZATION IN CLINICAL-TRIALS
复制标题
DOI：
10.1016/0197-2456(88)90046-3
复制
发表时间：
1988-12-01
期刊：
影响因子：
--
通讯作者：
LACHIN, JM
中科院分区：
文献类型：
--
作者：
LACHIN, JM
本文给出了完全随机化(如掷硬币)和随机分配规则(n个元素的n/2的随机排列)的性质。后者主要用于总样本量n是先验精确已知的情况。治疗不平衡的可能性很容易计算，在大型试验(n>；200)中可以忽略不计，无论是否使用分层随机分组。结果表明，在大型试验中，出现实质性治疗失衡的可能性极小，因此很可能不会对功率产生实质性影响。对于完全随机化的无条件和有条件随机分布以及随机分配规则，给出了线性秩检验族的大样本排列分布。渐近地，这三种方法等同于基于抽样的总体模型下这些检验的分布。还提出了基于协变量在一个或多个后定义的患者亚组内进行分层分析的排列测试。当一些患者的反应被假定为随机缺失时，这为分析提供了基础。使用Blackwell-Hodges模型，结果表明，完全随机化消除了选择偏差的可能性，但随机分配规则在非掩蔽试验中产生了很大的选择偏差的可能性。最后，使用意外偏差的Efron模型来评估由于协变量不平衡而导致的治疗效果估计中的偏差的可能性。渐近地，对于完全随机化和随机分配规则，该概率接近于零。然而，对于有限的n，完全随机化将意外偏差的概率降至最低，而使用随机分配规则时，该概率略高。结论：完全随机化在大型临床试验中具有优势。
This article presents the properties of complete randomization (e.g., coin toss) and of the random allocation rule (random permutation of n/2 of n elements). The latter is principally used in cases where the total sample size n is known exactly a priori. The likelihood of treatment imbalances is readily computed and is shown to be negligible for large trials (n > 200), regardless of whether a stratified randomization is used. It is shown that substantial treatment imbalances are extremely unlikely in large trials, and therefore there is likely to be no substantial effect on power. The large-sample permutational distribution of the family of linear rank tests is presented for complete randomization unconditionally and conditionally, and for the random allocation rule. Asymptotically the three are equivalent to the distribution of these tests under a sampling-based population model. Permutation tests are also presented for a stratified analysis within one or more subgroups of patients defined post hoc on the basis of a covariate. This provides a basis for analysis when some patients'' responses are assumed to be missing-at-random. Using the Blackwell-Hodges model, it is shown that complete randomization eliminates the potential for selection bias, but that the random allocation rule yields a substantial potential for selection bias in an unmasked trial. Finally, the Efron model for accidental bias is used to assess the potential for bias in the estimate of treatment effect due to covariate imabalance. Asymptotically, this probability approaches zero for complete randomization and for the random allocation rule. However, for finite n, complete randomization minimizes the probability of accidental bias, whereas this probability is slightly higher with a random allocation rule. It is concluded that complete randomization has merit in large clinical trials.