Reweighted Mahalanobis distance matching for cluster-randomized trials with missing data.
Reweighted Mahalanobis distance matching for cluster-randomized trials with missing data.
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DOI：
10.1002/pds.3260
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发表时间：
2012-05
影响因子：
2.6
通讯作者：
Griffin MR
中科院分区：
文献类型：
--
作者：
Greevy RA Jr;Grijalva CG;Roumie CL;Beck C;Hung AM;Murff HJ;Liu X;Griffin MR
本文介绍了一种设计配对随机试验的改进工具。该工具允许结合临床和其他知识，关于在匹配中使用的变量的相对重要性，并允许多种类型的缺失数据。该方法是在一个集群随机试验的背景下说明。介绍了一个web应用程序和R包来实现该方法，并结合了该领域的最新进展。重新加权马氏距离（RMD）匹配结合了用户指定的权重和缺失数据的输入值。可以给缺失指标分配权重，以匹配缺失模式。本文给出了三个例子，使用了来自90个退伍军人健康管理局站点的真实数据，这些站点在2007年至少有100个二甲双胍事件使用者。匹配被用来平衡在站点级别聚合的七个因素。在每种策略下评估10,000个随机化的协变量平衡：简单随机化，使用马氏距离的匹配随机化，以及使用RMD的匹配随机化。RMD匹配比简单随机化或MD随机化实现了更好的平衡。在第一个例子中，简单和MD随机化导致有10%的机会看到每个部位非白人患者百分比的绝对平均差异大于26%；RMD将这一比例大幅降低至6%。即使有多达20%的数据丢失，RMD也比简单的随机化取得了显著的改进。RMD匹配提供了一个易于使用的工具，可以将用户知识和丢失的数据合并在一起。
This paper introduces an improved tool for designing matched-pairs randomized trials. The tool allows the incorporation of clinical and other knowledge regarding the relative importance of variables used in matching and allows for multiple types of missing data. The method is illustrated in the context of a cluster-randomized trial. A web application and R package are introduced to implement the method and incorporate recent advances in the area. Reweighted Mahalanobis Distance (RMD) matching incorporates user-specified weights and imputed values for missing data. Weight may be assigned to missingness indicators to match on missingness patterns. Three examples are presented, using real data from a cohort of 90 Veterans Health Administration sites that had at least 100 incident metformin users in 2007. Matching is utilized to balance seven factors aggregated at the site level. Covariate balance is assessed for 10,000 randomizations under each strategy: simple randomization, matched randomization using the Mahalanobis distance, and matched randomization using the RMD. The RMD matching achieved better balance than simple randomization or MD randomization. In the first example, simple and MD randomization resulted in a 10% chance of seeing an absolute mean difference of greater than 26% in the percent of nonwhite patients per site; the RMD dramatically reduced that to 6%. The RMD achieved significant improvement over simple randomization even with as much as 20% of the data missing. RMD matching provides an easy-to-use tool that incorporates user knowledge and missing data.