REVSTAT-Statistical JournalREVSTAT-统计杂志
REVSTAT-Statistical Journal(英文缩写 REVSTAT-STAT J),ISSN 1645-6726,eISSN 2183-0371,中文译名:REVSTAT-统计杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 0.985 | Q4 |
| 2022 | 0.900 | Q4 |
| 2023 | 0.700 | Q3 |
| 2024 | 1.200 | Q2 |
| 2025 | 1.300 | Q2 |
REVSTAT-Statistical Journal 最新收录文献
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1. Continuous Post-Market Sequential Safety Surveillance with Minimum Events to Signal.
PMID:日期:2017-07The 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.
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2. MISSING DATA IN REGRESSION MODELS FOR NON-COMMENSURATE MULTIPLE OUTCOMES.
PMID:日期:2011-03-01Biomedical 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.