Division of Pharmacoepidemiology and Pharmacoeconomics; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:69-80. doi: 10.1002/pds.3263.
Among the large number of cohort studies that employ propensity score matching, most match patients 1:1. Increasing the matching ratio is thought to improve precision but may come with a trade-off with respect to bias.
To evaluate several methods of propensity score matching in cohort studies through simulation and empirical analyses.
We simulated cohorts of 20,000 patients with exposure prevalence of 10%-50%. We simulated five dichotomous and five continuous confounders. We estimated propensity scores and matched using digit-based greedy ("greedy"), pairwise nearest neighbor within a caliper ("nearest neighbor"), and a nearest neighbor approach that sought to balance the scores of the comparison patient above and below that of the treated patient ("balanced nearest neighbor"). We matched at both fixed and variable matching ratios and also evaluated sequential and parallel schemes for the order of formation of 1:n match groups. We then applied this same approach to two cohorts of patients drawn from administrative claims data.
Increasing the match ratio beyond 1:1 generally resulted in somewhat higher bias. It also resulted in lower variance with variable ratio matching but higher variance with fixed. The parallel approach generally resulted in higher mean squared error but lower bias than the sequential approach. Variable ratio, parallel, balanced nearest neighbor matching generally yielded the lowest bias and mean squared error.
1:n matching can be used to increase precision in cohort studies. We recommend a variable ratio, parallel, balanced 1:n, nearest neighbor approach that increases precision over 1:1 matching at a small cost in bias.
在大量采用倾向评分匹配的队列研究中，大多数研究将患者进行 1:1 匹配。增加匹配比例被认为可以提高精度，但可能会在偏差方面存在权衡。
通过模拟和实证分析评估队列研究中几种倾向评分匹配方法。
我们模拟了 20000 名患者的队列，暴露率为 10%-50%。我们模拟了五个二分类和五个连续混杂因素。我们估计了倾向评分并进行了匹配，使用基于数字的贪婪匹配（“贪婪”）、卡尺内的最近邻匹配（“最近邻”）和试图平衡治疗患者和对照患者评分的最近邻匹配（“平衡最近邻”）。我们在固定和可变匹配比例下进行了匹配，并评估了 1:n 匹配组形成的顺序的顺序和并行方案。然后，我们将这种方法应用于从行政索赔数据中提取的两个患者队列。
将匹配比例增加到 1:1 以上通常会导致稍微更高的偏差。它还导致可变比例匹配的方差降低，但固定比例匹配的方差增加。并行方法通常会导致更高的均方误差，但偏差低于顺序方法。可变比例、并行、平衡最近邻匹配通常会产生最低的偏差和均方误差。
1:n 匹配可用于提高队列研究的精度。我们建议使用可变比例、并行、平衡的 1:n 最近邻方法，在小的偏差成本下提高 1:1 匹配的精度。
