Optimal multivariate matching before randomization
Optimal multivariate matching before randomization
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DOI：
10.1093/biostatistics/5.2.263
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发表时间：
2004-04-01
期刊：
影响因子：
2.1
通讯作者：
Rosenbaum, P
中科院分区：
文献类型：
--
作者：
Greevy, R;Lu, B;Rosenbaum, P
虽然随机化前的区组或配对是实验设计的基本原则，但该原则几乎总是适用于至多一个或两个区组变量。在这里，我们讨论了使用最佳的多变量匹配之前，随机化，以提高协变量的平衡，在同一时间，提出了一个算法和案例研究其性能。当所有受试者或大组受试者同时接受随机化时，该方法非常有用。最佳匹配将一组2n个受试者分成n对，以最大限度地减少配对内的协变量差异-所谓的非二分匹配问题-然后在每对中随机挑选一个受试者进行治疗，另一个被分配给控制。使用基线协变量数据的132例患者从一个实际的，不匹配的，随机实验，我们构建66对匹配的14个协变量。然后，我们创建10000不匹配和10000匹配的随机实验，通过重复随机化的132例患者，并比较协变量的平衡与不匹配。通过每项测量，当在匹配对内进行随机化时，14个协变量中的每一个都基本上更好地平衡。即使在对14个协变量的机会不平衡进行协方差调整后，匹配的随机化也比不匹配的随机化提供了更准确的估计值，准确性的增加相当于平均增加7%的样本量。在无治疗效应的随机化检验中，即使14个协变量中只有2个与模拟应答相关，使用符号秩检验的匹配随机化的把握度也显著高于使用秩和检验的不匹配随机化。不匹配的随机化经历了罕见的灾难，而匹配的随机化则一直避免了这种灾难。
Although blocking or pairing before randomization is a basic principle of experimental design, the principle is almost invariably applied to at most one or two blocking variables. Here, we discuss the use of optimal multivariate matching prior to randomization to improve covariate balance for many variables at the same time, presenting an algorithm and a case-study of its performance. The method is useful when all subjects, or large groups of subjects, are randomized at the same time. Optimal matching divides a single group of 2n subjects into n pairs to minimize covariate differences within pairs-the so-called nonbipartite matching problem-then one subject in each pair is picked at random for treatment, the other being assigned to control. Using the baseline covariate data for 132 patients from an actual, unmatched, randomized experiment, we construct 66 pairs matching for 14 covariates. We then create 10000 unmatched and 10000 matched randomized experiments by repeatedly randomizing the 132 patients, and compare the covariate balance with and without matching. By every measure, every one of the 14 covariates was substantially better balanced when randomization was performed within matched pairs. Even after covariance adjustment for chance imbalances in the 14 covariates, matched randomizations provided more accurate estimates than unmatched randomizations, the increase in accuracy being equivalent to, on average, a 7% increase in sample size. In randomization tests of no treatment effect, matched randomizations using the signed rank test had substantially higher power than unmatched randomizations using the rank sum test, even when only 2 of 14 covariates were relevant to a simulated response. Unmatched randomizations experienced rare disasters which were consistently avoided by matched randomizations.