Linden Consulting Group, Ann Arbor, Michigan, USA; Department of Health Policy & Management, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA.
J Eval Clin Pract. 2013 Oct;19(5):968-75. doi: 10.1111/jep.12072. Epub 2013 Aug 3.
When a randomized controlled trial is not feasible, investigators typically turn to matching techniques as an alternative approach to evaluate the effectiveness of health care interventions. Matching studies are designed to minimize imbalances on measured pre-intervention characteristics, thereby reducing bias in estimates of treatment effects. Generally, a matching ratio up to 4:1 (control to treatment) elicits the lowest bias. However, when matching techniques are used in prospective studies, investigators try to maximize the number of controls matched to each treated individual to increase the likelihood that a sufficient sample size will remain after attrition. In this paper, we describe a systematic approach to managing the trade-off between minimizing bias and maximizing matched sample size. Our approach includes the following three steps: (1) run the desired matching algorithm, starting with 1:1 (one control to one treated individual) matching and iterating until the maximum desired number of potential controls per treated subject is reached; (2) for each iteration, test for covariate balance; and (3) generate numeric summaries and graphical plots of the balance statistics across all iterations in order to determine the optimal solution. We demonstrate the implementation of this approach with data from a medical home pilot programme and with a simulation study of populations of 100,000 in which 1000 individuals receive the intervention. We advocate undertaking this methodical approach in matching studies to ensure that the optimal matching solution is identified. Doing so will raise the overall quality of the literature and increase the likelihood of identifying effective interventions.
当无法进行随机对照试验时，研究人员通常会转向匹配技术作为评估医疗干预措施效果的替代方法。匹配研究旨在最大程度地减少干预前测量特征的不平衡，从而减少治疗效果估计的偏差。通常，匹配比高达 4:1（对照与治疗）可以产生最低的偏差。然而，当匹配技术用于前瞻性研究时，研究人员试图最大化每个治疗个体匹配的对照数量，以增加在流失后仍保留足够样本量的可能性。在本文中，我们描述了一种系统的方法来权衡最小化偏差和最大化匹配样本量之间的关系。我们的方法包括以下三个步骤：（1）运行所需的匹配算法，从 1:1（一个对照与一个治疗个体）匹配开始，并迭代，直到达到每个治疗个体的最大期望对照数量；（2）对于每个迭代，测试协变量平衡；（3）生成所有迭代中平衡统计数据的数值摘要和图形图，以确定最佳解决方案。我们通过医疗家庭试点计划的数据和一个模拟研究 100,000 人的人群数据来演示这种方法的实现，其中 1000 人接受干预。我们主张在匹配研究中采用这种有条不紊的方法，以确保确定最佳匹配解决方案。这样做将提高文献的整体质量，并增加确定有效干预措施的可能性。
