{"title":"通过:计算两个样本测试的功率和样本量","authors":"Jinpu Li, R. Knigge, Kaiyi Chen, E. Leary","doi":"10.32614/rj-2021-094","DOIUrl":null,"url":null,"abstract":"Power and sample size estimation are critical aspects in study design to demonstrate minimized risk for subjects and to justify the allocation of time, money, and other resources. Researchers often work with response variables which take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.","PeriodicalId":20974,"journal":{"name":"R J.","volume":"45 1","pages":"450"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"PASSED: Calculate Power and Sample Size for Two Sample Tests\",\"authors\":\"Jinpu Li, R. Knigge, Kaiyi Chen, E. Leary\",\"doi\":\"10.32614/rj-2021-094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Power and sample size estimation are critical aspects in study design to demonstrate minimized risk for subjects and to justify the allocation of time, money, and other resources. Researchers often work with response variables which take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.\",\"PeriodicalId\":20974,\"journal\":{\"name\":\"R J.\",\"volume\":\"45 1\",\"pages\":\"450\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"R J.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.32614/rj-2021-094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"R J.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32614/rj-2021-094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
PASSED: Calculate Power and Sample Size for Two Sample Tests
Power and sample size estimation are critical aspects in study design to demonstrate minimized risk for subjects and to justify the allocation of time, money, and other resources. Researchers often work with response variables which take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.