Combining propensity score matching and group-based trajectory analysis in an observational study
Combining propensity score matching and group-based trajectory analysis in an observational study
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DOI：
10.1037/1082-989x.12.3.247
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发表时间：
2007-09-01
影响因子：
7
通讯作者：
Rosenbaum, Paul R.
中科院分区：
文献类型：
--
作者：
Haviland, Amelia;Nagin, Daniel S.;Rosenbaum, Paul R.
在非随机或观察性研究中，倾向评分可用于平衡观察到的协变量，轨迹组可用于控制基线或治疗前的结果测量。轨迹组还有助于表征没有良好匹配的受试者类别，并定义治疗效果可能不同的实质上有趣的组。使用蒙特利尔研究的数据说明了这些方法和相关方法。研究了 14 岁时加入帮派对随后暴力行为的影响，同时控制了 14 岁之前男孩的测量特征。根据 11 岁到 13 岁的暴力行为，将男孩分为轨迹组。在轨迹组内，使用倾向得分、马哈拉诺比斯距离和组合优化算法，将加入者与可变数量的控制进行最佳匹配。使用可变比率匹配比成对匹配具有更高的效率，并且比固定比率匹配更能减少偏差。使用敏感性分析检查未能调整重要但未测量的协变量可能产生的影响。
In a nonrandomized or observational study, propensity scores may be used to balance observed covariates and trajectory groups may be used to control baseline or pretreatment measures of outcome. The trajectory groups also aid in characterizing classes of subjects for whom no good matches are available and to define substantively interesting groups between which treatment effects may vary. These and related methods are illustrated using data from a Montreal-based study. The effects on subsequent violence of gang joining at age 14 are studied while controlling for measured characteristics of boys prior to age 14. The boys are divided into trajectory groups based on violence from ages I I to 13. Within trajectory group, joiners are optimally matched to a variable number of controls using propensity scores, Mahalanobis distances, and a combinatorial optimization algorithm. Use of variable ratio matching results in greater efficiency than pair matching and also greater bias reduction than matching at a fixed ratio. The possible impact of failing to adjust for an important but unmeasured covariate is examined using sensitivity analysis.