VA Tennessee Valley Geriatric Research Education Clinical Center (GRECC), Nashville, TN, USA.
Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2(0 2):148-54. doi: 10.1002/pds.3260.
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 an 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.
Reweighted Mahalanobis distance matching provides an easy-to-use tool that incorporates user knowledge and missing data.
本文介绍了一种改进的匹配对随机试验设计工具。该工具允许纳入与匹配使用的变量的相对重要性有关的临床和其他知识，并允许多种类型的缺失数据。该方法在一个群组随机试验的背景下进行了说明。介绍了一个 Web 应用程序和一个 R 包，以实现该方法并结合该领域的最新进展。
重加权马氏距离（RMD）匹配纳入了用户指定的权重和对缺失数据的插补值。可以为缺失指标分配权重，以匹配缺失模式。使用来自退伍军人健康管理局的 90 个站点队列的真实数据，这些站点在 2007 年至少有 100 名新使用二甲双胍的患者，呈现了三个示例。使用站点级别聚合的七个因素进行匹配以实现平衡。在每种策略下对 10,000 次随机化进行了协变量平衡评估：简单随机化、使用马氏距离进行匹配的随机化和使用 RMD 进行匹配的随机化。
RMD 匹配比简单随机化或 MD 随机化实现了更好的平衡。在第一个示例中，简单和 MD 随机化导致每个站点的非白人患者比例的绝对平均差异大于 26%的可能性为 10%；RMD 将该比例大大降低至 6%。即使缺失了多达 20%的数据，RMD 仍比简单随机化显著改善。
重加权马氏距离匹配提供了一种易于使用的工具，可纳入用户知识和缺失数据。
