匹配调整间接比较用于生存数据分析:在R语言中的实现

Matching-adjusted indirect comparison for survival data analysis:implementation in R language

随着单臂临床试验日益增多,缺乏头对头临床研究,未调整的间接比较和网状Meta分析应用局限性日益突出。当个体数据数量有限时,匹配调整间接比较能够充分地利用单项研究个体数据并通过倾向得分匹配其他研究的汇总数据,有效地平衡不同试验中人群差异带来的潜在偏倚,并完成目标干预措施间的疗效比较。本研究一方面围绕匹配调整间接比较有关概念和原理进行介绍,另一方面,基于当前肿瘤药物评价的广泛应用,系统展示如何基于R语言使用锚定匹配调整间接比较法针对生存数据进行分析,以期为科学循证决策提供参考。

With the increase in the number of single-arm clinical trials and lack of head-to-head clinical studies,the application of unadjusted indirect comparisons and network meta-analysis methods has been limited.Matchingadjusted indirect comparison(MAIC)is an alternative method to fully utilize individual patient data from one study and balance potential bias caused by baseline characteristics differences in different trials through propensity score matching with aggregated data reported in other studies,and complete the comparison of the efficacy between target interventions.This study introduced the concept and principles of MAIC.In addition,we demonstrated how to use the anchored MAIC method based on R language for survival data,which has been widely used in anti-cancer drug evaluation.This study aimed to provide an alternative method to inform evidence-based decisions.