Does matching introduce confounding or selection bias into the matched case-control design?

Fei Wan, S. Sutcliffe, Jeffrey Zhang, Dylan Small
{"title":"Does matching introduce confounding or selection bias into the matched case-control design?","authors":"Fei Wan, S. Sutcliffe, Jeffrey Zhang, Dylan Small","doi":"10.1353/obs.2024.a929114","DOIUrl":null,"url":null,"abstract":"Abstract:The impact of matching on confounding control in case-control studies remains a subject of ongoing debate, with varying perspectives among researchers. While matching is a well-established method for controlling confounding in cohort studies, its effectiveness in mitigating confounding in case-control studies has long been questioned. Recent studies have determined that matching doesn't eliminate confounding but, instead, introduces a selection bias on top of the initial confounding, as indicated by causal diagram analysis. This conclusion suggests that the control of initial confounding through matching is either only partial or non-existent. However, this conclusion may not be accurate in exactly matched design because causal diagram cannot always reveal precisely the interplay between the initial confounding and the matching induced selection effect. In this paper, we employ analytical results in conjunction with causal diagrams to demonstrate that the cancellation of the initial confounding by the selection effect is complete in exact individually matched case-control studies. Nevertheless, this cancellation results in a residual selection effect that establishes a backdoor connection between the matching factors and the outcome in the matched design. Failure to adjust for this residual selection effect leads to biased estimates of the exposure effect. Furthermore, this backdoor connection causes matching factors to act like confounding factors in the matched case-control design, which complicates the interpretation of the bias introduced by matching in current literature.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Observational studies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/obs.2024.a929114","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract:The impact of matching on confounding control in case-control studies remains a subject of ongoing debate, with varying perspectives among researchers. While matching is a well-established method for controlling confounding in cohort studies, its effectiveness in mitigating confounding in case-control studies has long been questioned. Recent studies have determined that matching doesn't eliminate confounding but, instead, introduces a selection bias on top of the initial confounding, as indicated by causal diagram analysis. This conclusion suggests that the control of initial confounding through matching is either only partial or non-existent. However, this conclusion may not be accurate in exactly matched design because causal diagram cannot always reveal precisely the interplay between the initial confounding and the matching induced selection effect. In this paper, we employ analytical results in conjunction with causal diagrams to demonstrate that the cancellation of the initial confounding by the selection effect is complete in exact individually matched case-control studies. Nevertheless, this cancellation results in a residual selection effect that establishes a backdoor connection between the matching factors and the outcome in the matched design. Failure to adjust for this residual selection effect leads to biased estimates of the exposure effect. Furthermore, this backdoor connection causes matching factors to act like confounding factors in the matched case-control design, which complicates the interpretation of the bias introduced by matching in current literature.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
匹配病例对照设计是否会引入混杂或选择偏差?
摘要:在病例对照研究中,配对对混杂控制的影响一直是一个争论不休的话题,研究人员的观点也不尽相同。在队列研究中,配对是一种行之有效的混杂控制方法,但在病例对照研究中,配对在减少混杂方面的效果却一直受到质疑。最近的研究发现,匹配并不能消除混杂,反而会在初始混杂的基础上引入选择偏倚,因果图分析表明了这一点。这一结论表明,通过配对对初始混杂的控制要么只是部分的,要么根本不存在。然而,这一结论在完全匹配的设计中可能并不准确,因为因果图并不能总是精确地揭示初始混杂和匹配诱导的选择效应之间的相互作用。在本文中,我们将分析结果与因果图结合起来,证明在精确个体匹配的病例对照研究中,选择效应对初始混杂的抵消是完全的。然而,这种抵消会导致残余选择效应,在匹配设计中建立起匹配因素与结果之间的后门联系。如果不对这种残余选择效应进行调整,就会导致对暴露效应的估计出现偏差。此外,这种后门联系会使匹配因素在匹配病例对照设计中起到类似混杂因素的作用,从而使目前文献中对匹配所带来的偏差的解释变得复杂。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
0.80
自引率
0.00%
发文量
0
期刊最新文献
Does matching introduce confounding or selection bias into the matched case-control design? Size-biased sensitivity analysis for matched pairs design to assess the impact of healthcare-associated infections A Software Tutorial for Matching in Clustered Observational Studies Using a difference-in-difference control trial to test an intervention aimed at increasing the take-up of a welfare payment in New Zealand Estimating Treatment Effect with Propensity Score Weighted Regression and Double Machine Learning
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1