广义倾向得分匹配与成对倾向得分匹配的应用及比较
Application and comparison of generalized propensity score matching versus pairwise propensity score matching.
作者信息
Cui Zhanglin L, Hess Lisa M, Goodloe Robert, Faries Doug
机构信息
Global Patient Outcomes & Real World Evidence, Eli Lilly & Company, Indianapolis, IN 46285, USA.
出版信息
J Comp Eff Res. 2018 Sep;7(9):923-934. doi: 10.2217/cer-2018-0030. Epub 2018 Jun 21.
AIM
A comparison of conventional pairwise propensity score matching (PSM) and generalized PSM method was applied to the comparative effectiveness of multiple treatment options for lung cancer.
MATERIALS & METHODS: Deidentified data were analyzed. Covariate balances between compared treatments were assessed before and after PSM. Cox proportional hazards regression compared overall survival after PSM.
RESULTS & CONCLUSION: The generalized PSM analyses were able to retain 61.2% of patients, while the conventional PSM analyses were able to match from 24.1 to 77.1% of patients from each treatment comparison. The generalized PSM achieved statistical significance (p < 0.05) in 8/10 comparisons, whereas conventional pairwise PSM achieved 1/10. The noted differences arose from different matched patient samples and the size of the samples.
目的
将传统的成对倾向评分匹配（PSM）方法与广义PSM方法进行比较，以评估肺癌多种治疗方案的相对疗效。
材料与方法
对匿名数据进行分析。在PSM前后评估比较治疗之间的协变量平衡。Cox比例风险回归比较PSM后的总生存期。
结果与结论
广义PSM分析能够保留61.2%的患者，而传统PSM分析在每次治疗比较中能够匹配24.1%至77.1%的患者。广义PSM在10次比较中有8次达到统计学显著性（p < 0.05），而传统成对PSM仅在10次比较中的1次达到。上述差异源于匹配患者样本的不同以及样本量的大小。
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