{"title":"随机过程在临床试验参与者招募中的应用","authors":"Rickey Edward Carter","doi":"10.1016/j.cct.2004.07.002","DOIUrl":null,"url":null,"abstract":"<div><p>The allocation of sufficient time for participant recruitment is one of the fundamental aspects in planning a clinical trial. This paper illustrates how a Poisson process can be used to determine an optimal period of time for participant recruitment by simulating Poisson arrival into a clinical trial. The simulation study provides the means to generate of an empirical probability density function for the recruitment time based on time-dependent changes in the accrual rate. From this empirical distribution, a clinical trial recruitment period can be planned to provide a high level of confidence (e.g., 90% probability) of enrolling the sample size within the planned amount of time given the simulation assumptions.</p></div>","PeriodicalId":72706,"journal":{"name":"Controlled clinical trials","volume":"25 5","pages":"Pages 429-436"},"PeriodicalIF":0.0000,"publicationDate":"2004-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.cct.2004.07.002","citationCount":"43","resultStr":"{\"title\":\"Application of stochastic processes to participant recruitment in clinical trials\",\"authors\":\"Rickey Edward Carter\",\"doi\":\"10.1016/j.cct.2004.07.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The allocation of sufficient time for participant recruitment is one of the fundamental aspects in planning a clinical trial. This paper illustrates how a Poisson process can be used to determine an optimal period of time for participant recruitment by simulating Poisson arrival into a clinical trial. The simulation study provides the means to generate of an empirical probability density function for the recruitment time based on time-dependent changes in the accrual rate. From this empirical distribution, a clinical trial recruitment period can be planned to provide a high level of confidence (e.g., 90% probability) of enrolling the sample size within the planned amount of time given the simulation assumptions.</p></div>\",\"PeriodicalId\":72706,\"journal\":{\"name\":\"Controlled clinical trials\",\"volume\":\"25 5\",\"pages\":\"Pages 429-436\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.cct.2004.07.002\",\"citationCount\":\"43\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Controlled clinical trials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0197245604000522\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Controlled clinical trials","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0197245604000522","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Application of stochastic processes to participant recruitment in clinical trials
The allocation of sufficient time for participant recruitment is one of the fundamental aspects in planning a clinical trial. This paper illustrates how a Poisson process can be used to determine an optimal period of time for participant recruitment by simulating Poisson arrival into a clinical trial. The simulation study provides the means to generate of an empirical probability density function for the recruitment time based on time-dependent changes in the accrual rate. From this empirical distribution, a clinical trial recruitment period can be planned to provide a high level of confidence (e.g., 90% probability) of enrolling the sample size within the planned amount of time given the simulation assumptions.