Covariate-based constrained randomization of group-randomized trials.
Covariate-based constrained randomization of group-randomized trials.
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DOI：
10.1191/1740774504cn024oa
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发表时间：
2004-01-01
期刊：
影响因子：
--
通讯作者：
Moulton, Lawrence H
中科院分区：
文献类型：
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作者：
Moulton, Lawrence H
当个体随机化设计不可能或无法估计感兴趣的参数时，分组随机化研究设计是有用的。已经使用了区组和/或分层（例如，配对）设计，许多研究人员对它们的特性进行了统计评估。分组随机化试验通常具有少量的实验单元，以及强烈的地理诱导的单元间相关性，这增加了获得“坏”随机化结果的机会。本文描述了一个程序-从可接受的分配列表中随机选择-以确保相关协变量平衡的方式分配治疗条件。许多个体和组水平的协变量可以使用精确或卡尺标准进行平衡。仿真结果表明，该方法具有良好的频率特性，但可能需要注意不要过度约束随机化。在通过高度约束的设计实现良好平衡与危害调查者公正性的外观以及可能偏离标称I类错误之间存在权衡。
Group-randomized study designs are useful when individually randomized designs are either not possible, or will not be able to estimate the parameters of interest. Blocked and/or stratified (for example, pair-matched) designs have been used, and their properties statistically evaluated by many researchers. Group-randomized trials often have small numbers of experimental units, and strong, geographically induced between-unit correlation, which increase the chance of obtaining a "bad" randomization outcome. This article describes a procedure--random selection from a list of acceptable allocations--to allocate treatment conditions in a way that ensures balance on relevant covariates. Numerous individual- and group-level covariates can be balanced using exact or caliper criteria. Simulation results indicate that this method has good frequency properties, but some care may be needed not to overly constrain the randomization. There is a trade-off between achieving good balance through a highly constrained design, and jeopardizing the appearance of impartiality of the investigator and potentially departing from the nominal Type I error.