One-to-many propensity score matching in cohort studies
One-to-many propensity score matching in cohort studies
复制标题
DOI：
10.1002/pds.3263
复制
发表时间：
2012-05-01
影响因子：
2.6
通讯作者：
Schneeweiss, Sebastian
中科院分区：
文献类型：
--
作者：
Rassen, Jeremy A.;Shelat, Abhi A.;Schneeweiss, Sebastian
背景：在大量采用倾向评分匹配的队列研究中，大多数患者1：1匹配。增加匹配比被认为可以提高精确度，但可能会在偏倚方面进行权衡。目的通过模拟和实证分析，对队列研究中几种倾向得分匹配方法进行评价。方法我们模拟了暴露患病率为10%~50%的2万名患者的队列。我们模拟了五个二分式和五个连续混合法。我们评估了倾向性分数，并使用基于数字的贪婪(贪婪)、卡尺内的成对最近邻居(最近邻居)和最近邻居方法进行匹配，该方法试图平衡比较患者的分数高于和低于治疗患者的分数(平衡最近邻居)。我们在固定和可变匹配比率下进行匹配，并对1：N匹配组的形成顺序的顺序和并行方案进行了评估。然后，我们将同样的方法应用于从管理索赔数据中提取的两组患者。结果将匹配比提高到1：1以上，一般会产生较高的偏倚。变比匹配的方差较低，固定比匹配的方差较高。与序贯方法相比，并行方法一般具有更高的均方误差但更低的偏差。可变比例、并行、平衡的最近邻匹配通常产生最低的偏差和均方误差。结论1：N配型可提高队列研究的精确度。我们推荐一种可变比例、并行、平衡的1：N近邻方法，该方法以较小的偏差代价提高了比1：1匹配更高的精度。版权所有(C)2012 John Wiley&Sons，Ltd.
Background 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. Objective To evaluate several methods of propensity score matching in cohort studies through simulation and empirical analyses. Methods 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. Results 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. Conclusions 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. Copyright (C) 2012 John Wiley & Sons, Ltd.