Academy of Mathematics and System Sciences, Chinese Academy of Science, Beijing, China.
Department of Statistics, College of Mathematics, Southwest Jiaotong University, Chengdu, China.
Stat Med. 2021 Aug 30;40(19):4252-4268. doi: 10.1002/sim.9026. Epub 2021 May 11.
Since the outbreak of the new coronavirus disease (COVID-19), a large number of scientific studies and data analysis reports have been published in the International Journal of Medicine and Statistics. Taking the estimation of the incubation period as an example, we propose a low-cost method to integrate external research results and available internal data together. By using empirical likelihood method, we can effectively incorporate summarized information even if it may be derived from a misspecified model. Taking the possible uncertainty in summarized information into account, we augment a logarithm of the normal density in the log empirical likelihood. We show that the augmented log-empirical likelihood can produce enhanced estimates for the underlying parameters compared with the method without utilizing auxiliary information. Moreover, the Wilks' theorem is proved to be true. We illustrate our methodology by analyzing a COVID-19 incubation period data set retrieved from Zhejiang Province and summarized information from a similar study in Shenzhen, China.
自新型冠状病毒病（COVID-19）爆发以来，《国际医学与统计杂志》发表了大量的科学研究和数据分析报告。以潜伏期估计为例，我们提出了一种将外部研究结果和可用内部数据整合在一起的低成本方法。通过使用经验似然方法，即使汇总信息可能来自指定不当的模型，我们也可以有效地合并汇总信息。考虑到汇总信息中可能存在的不确定性，我们在对数经验似然中增加了一个正态密度的对数。我们表明，与不利用辅助信息的方法相比，增加的对数经验似然可以对基础参数产生增强的估计。此外，证明了威尔克斯定理是正确的。我们通过分析从中国浙江省检索到的 COVID-19 潜伏期数据集和中国深圳类似研究的汇总信息来说明我们的方法。
