{"title":"Multiplicity Control in Clinical Trials.","authors":"Amy LaLonde, Steven E Nissen","doi":"10.1056/EVIDctw2400393","DOIUrl":null,"url":null,"abstract":"<p><p>AbstractStatistical testing of more than one hypothesis has the potential to increase the risk of wrongly concluding that the result for a given end point is statistically significant (false discovery). This review is designed to acquaint nonstatisticians with traditional approaches for controlling type I error and with the seemingly complex procedure known as graphical testing.</p>","PeriodicalId":74256,"journal":{"name":"NEJM evidence","volume":"4 8","pages":"EVIDctw2400393"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"NEJM evidence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1056/EVIDctw2400393","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/22 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
AbstractStatistical testing of more than one hypothesis has the potential to increase the risk of wrongly concluding that the result for a given end point is statistically significant (false discovery). This review is designed to acquaint nonstatisticians with traditional approaches for controlling type I error and with the seemingly complex procedure known as graphical testing.
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