论文标题
将人口和研究数据结合起来，以推断事件速率
Combining Population and Study Data for Inference on Event Rates
论文作者
论文摘要
本说明考虑了对经历某些事件的人群中某个子群体中个人份额进行统计推断的问题。具体的并发症是需要估算子组的大小，而经历事件的个人数量是已知的。该问题是由Streeck等人最近的研究激发的。 （2020年），估计德国城镇中SARS-COV-2感染的感染死亡率（IFR）在2020年2月中旬经历了超级宣传事件。在他们的情况下，感兴趣的亚组由所有感染者组成，并且该事件是由感染引起的。我们在此上下文中使用目标参数的精确定义阐明了问题，并根据经典的统计原理提出置信区间（CI），从而带来良好的覆盖范围。
This note considers the problem of conducting statistical inference on the share of individuals in some subgroup of a population that experience some event. The specific complication is that the size of the subgroup needs to be estimated, whereas the number of individuals that experience the event is known. The problem is motivated by the recent study of Streeck et al. (2020), who estimate the infection fatality rate (IFR) of SARS-CoV-2 infection in a German town that experienced a super-spreading event in mid-February 2020. In their case the subgroup of interest is comprised of all infected individuals, and the event is death caused by the infection. We clarify issues with the precise definition of the target parameter in this context, and propose confidence intervals (CIs) based on classical statistical principles that result in good coverage properties.
