Revstat-Statistical Journal最新文献

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Endemic-epidemic framework used in covid-19 modelling (Discussion on the paper by nunes, caetano, antunes and dias) covid-19建模中使用的地方性流行病框架(由nunes, caetano, antunes和dias对论文进行讨论)
IF 0.9 4区 数学
Revstat-Statistical Journal Pub Date : 2020-10-01 DOI: 10.5167/UZH-198715
M. Dunbar, L. Held
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引用次数: 6
Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal. 用最小的事件信号进行连续的上市后顺序安全监控。
IF 0.9 4区 数学
Revstat-Statistical Journal Pub Date : 2017-07-01
Martin Kulldorff, Ivair R Silva
{"title":"Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal.","authors":"Martin Kulldorff,&nbsp;Ivair R Silva","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The CDC Vaccine Safety Datalink project has pioneered the use of near real-time post-market vaccine safety surveillance for the rapid detection of adverse events. Doing weekly analyses, continuous sequential methods are used, allowing investigators to evaluate the data near-continuously while still maintaining the correct overall alpha level. With continuous sequential monitoring, the null hypothesis may be rejected after only one or two adverse events are observed. In this paper, we explore continuous sequential monitoring when we do not allow the null to be rejected until a minimum number of observed events have occurred. We also evaluate continuous sequential analysis with a delayed start until a certain sample size has been attained. Tables with exact critical values, statistical power and the average times to signal are provided. We show that, with the first option, it is possible to both increase the power and reduce the expected time to signal, while keeping the alpha level the same. The second option is only useful if the start of the surveillance is delayed for logistical reasons, when there is a group of data available at the first analysis, followed by continuous or near-continuous monitoring thereafter.</p>","PeriodicalId":54459,"journal":{"name":"Revstat-Statistical Journal","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8363220/pdf/nihms-1067124.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39311674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
MISSING DATA IN REGRESSION MODELS FOR NON-COMMENSURATE MULTIPLE OUTCOMES. 非相称多结果回归模型中的缺失数据。
IF 0.9 4区 数学
Revstat-Statistical Journal Pub Date : 2011-03-01
Armando Teixeira-Pinto, Sharon-Lise Normand
{"title":"MISSING DATA IN REGRESSION MODELS FOR NON-COMMENSURATE MULTIPLE OUTCOMES.","authors":"Armando Teixeira-Pinto,&nbsp;Sharon-Lise Normand","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Biomedical research often involves the measurement of multiple outcomes in different scales (continuous, binary and ordinal). A common approach for the analysis of such data is to ignore the potential correlation among the outcomes and model each outcome separately. This can lead not only to loss of efficiency but also to biased estimates in the presence of missing data. We address the problem of missing data in the context of multiple non-commensurate outcomes. The consequences of missing data when using likelihood and quasi-likelihood methods are described, and an extension of these methods to the situation of missing observations in the outcomes is proposed. Two real data examples illustrate the methodology.</p>","PeriodicalId":54459,"journal":{"name":"Revstat-Statistical Journal","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2011-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3595565/pdf/nihms307399.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31315241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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