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Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information

文献类型：期刊论文

作者Jiang, Zhongfeng5; Yang, Baoying4; Qin, Jing3; Zhou, Yong1,2
刊名STATISTICS IN MEDICINE
出版日期2021-05-11
页码17
关键词augmented log‐ empirical likelihood COVID‐ 19 incubation period meta‐ analysis Wilks&apos theorem
ISSN号0277-6715
DOI10.1002/sim.9026
英文摘要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.
资助项目National Natural Science Foundation of China[11501472] ; State Key Program of National Natural Science Foundation of China[71931004]
WOS研究方向Mathematical & Computational Biology ; Public, Environmental & Occupational Health ; Medical Informatics ; Research & Experimental Medicine ; Mathematics
语种英语
WOS记录号WOS:000649037300001
出版者WILEY
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/58629]  
专题中国科学院数学与系统科学研究院
通讯作者Yang, Baoying
作者单位1.East China Normal Univ, Acad Stat & Interdisciplinary Sci, Shanghai, Peoples R China
2.MOE, Key Lab Adv Theory & Applicat Stat & Data Sci, Shanghai, Peoples R China
3.NIAID, NIH, 9000 Rockville Pike, Bethesda, MD 20892 USA
4.Southwest Jiaotong Univ, Dept Stat, Coll Math, Chengdu 611756, Sichuan, Peoples R China
5.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Zhongfeng,Yang, Baoying,Qin, Jing,et al. Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information[J]. STATISTICS IN MEDICINE,2021:17.
APA Jiang, Zhongfeng,Yang, Baoying,Qin, Jing,&Zhou, Yong.(2021).Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information.STATISTICS IN MEDICINE,17.
MLA Jiang, Zhongfeng,et al."Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information".STATISTICS IN MEDICINE (2021):17.

入库方式： OAI收割

来源：数学与系统科学研究院

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