{"title":"临床试验中的多重控制。","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":"{\"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}","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}
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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