Kalish L A, Begg C B
Control Clin Trials. 1987 Jun;8(2):121-35. doi: 10.1016/0197-2456(87)90037-7.
Complete randomization, the simplest method for allocating treatments to patients in clinical trials, can serve as a basis for inferential procedures using standard permutation tests, because the method ensures that each sequence of allocations is equally likely. No other method of allocation possesses this property. However, many clinical trials employ allocation methods that force balance of covariates across treatment groups. With these methods, some allocation sequences are impossible or highly unlikely so that standard permutation tests are technically invalidated. In this article we investigate whether standard permutation tests for binary outcomes are likely to yield distorted nominal p values in practical applications of these alternative allocation methods. A sample of completed trials conducted by the Eastern Cooperative Oncology Group serves as a basis on which to construct simulations. Our results indicate that nominal p values can be conservative, but are not likely to be severely distorted if the analysis is stratified by important covariates used as allocation prompts. Moreover the inherent conservativeness of exact methods due to discreteness tends to dominate any additional conservativeness due to nonrandom designs. In addition, we investigate the relationship of treatment allocation methods with bias in estimates from a logistic model when important covariates are unknown. This bias is the same for all asymptotically balanced allocation methods and is significant but not disastrous.
完全随机化是在临床试验中为患者分配治疗的最简单方法，它可以作为使用标准置换检验进行推断程序的基础，因为该方法可确保每个分配序列的可能性相同。没有其他分配方法具有此特性。然而，许多临床试验采用的分配方法会强制协变量在各治疗组间保持平衡。使用这些方法时，一些分配序列是不可能的或极不可能出现的，从而使标准置换检验在技术上无效。在本文中，我们研究了在这些替代分配方法的实际应用中，针对二元结果的标准置换检验是否可能产生扭曲的名义p值。东部肿瘤协作组进行的一组已完成试验样本作为构建模拟的基础。我们的结果表明，如果按用作分配提示的重要协变量进行分层分析，名义p值可能会保守，但不太可能严重扭曲。此外，由于离散性导致的精确方法的固有保守性往往会主导因非随机设计产生的任何额外保守性。此外，当重要协变量未知时，我们研究了治疗分配方法与逻辑模型估计偏差之间的关系。所有渐近平衡分配方法的这种偏差都是相同的，且偏差显著但并非灾难性的。
