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精确监测不良事件的人时比分布:历史监测与监测泊松数据。

The person-time ratio distribution for the exact monitoring of adverse events: Historical vs surveillance Poisson data.

机构信息

Department of Statistics, Federal University of Ouro Preto, Ouro Preto, Brazil.

Communicable Disease Control, Manitoba Health, Manitoba, Canada.

出版信息

Stat Med. 2023 Aug 15;42(18):3283-3301. doi: 10.1002/sim.9805. Epub 2023 May 23.

Abstract

In the postmarket drug and vaccine safety surveillance, when the number of adverse events follows a Poisson distribution, the ratio between the exposed and the unexposed person-time information is the random variable that governs the decision rule about the safety of the drug or vaccine. The probability distribution function of such a ratio is derived in this paper. Exact point and interval estimators for the relative risk are discussed as well as statistical hypothesis testing. To the best of our knowledge, this is the first paper that provides an unbiased estimator for the relative risk based on the person-time ratio. The applicability of this new distribution is illustrated through a real data analysis aimed to detect increased risk of occurrence of Myocarditis/Pericarditis following mRNA COVID-19 vaccination in Manitoba, Canada.

摘要

在药品和疫苗上市后的药物和疫苗安全性监测中,当不良事件的数量遵循泊松分布时,暴露组和非暴露组的人时信息之比是决定药物或疫苗安全性的随机变量。本文推导出了该比值的概率分布函数。还讨论了这种比率的相对风险的精确点估计和区间估计以及统计假设检验。据我们所知,这是第一篇基于人时比为相对风险提供无偏估计的论文。通过对加拿大马尼托巴省 mRNA COVID-19 疫苗接种后心肌炎/心包炎发生率增加风险的实际数据分析,说明了这种新分布的适用性。

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