Luke Keele, Matthew Lenard, Luke Miratrix, Lindsay Page
{"title":"A Software Tutorial for Matching in Clustered Observational Studies","authors":"Luke Keele, Matthew Lenard, Luke Miratrix, Lindsay Page","doi":"10.1353/obs.2023.a906624","DOIUrl":null,"url":null,"abstract":"Abstract:Many interventions occur in settings where treatments are applied to groups. For example, a math intervention may be implemented for all students in some schools and withheld from students in other schools. When such treatments are non-randomly allocated, researchers can use statistical adjustment to make treated and control groups similar in terms of observed characteristics. Recent work in statistics has developed a form of matching, known as multilevel matching, that is designed for contexts where treatments are clustered. In this article, we provide a tutorial on how to analyze clustered treatment using multilevel matching. We use a real data application to explain the full set of steps for the analysis of a clustered observational study.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-07","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.2023.a906624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract
Abstract:Many interventions occur in settings where treatments are applied to groups. For example, a math intervention may be implemented for all students in some schools and withheld from students in other schools. When such treatments are non-randomly allocated, researchers can use statistical adjustment to make treated and control groups similar in terms of observed characteristics. Recent work in statistics has developed a form of matching, known as multilevel matching, that is designed for contexts where treatments are clustered. In this article, we provide a tutorial on how to analyze clustered treatment using multilevel matching. We use a real data application to explain the full set of steps for the analysis of a clustered observational study.
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