Invited Commentary: Estimation and Bounds Under Data Fusion Comment

doi:10.1093/aje/kwab194

	Invited Commentary: Estimation and Bounds Under Data Fusion Comment
	Miao, Wang 4; Li, Wei 2,3; Hu, Wenjie 4; Wang, Ruoyu 1; Geng, Zhi 4
刊名	AMERICAN JOURNAL OF EPIDEMIOLOGY
	2021-07-07
页码	5
关键词	bounds data fusion epidemiologic methods imputation
ISSN号	0002-9262
DOI	10.1093/aje/kwab194
英文摘要	In their recent article, Ogburn et al. (Am J Epidemiol. 2021;190(6):1142-1147) raised a cautionary tale for epidemiologic data fusion: Bias may occur if a variable that is completely missing in the primary data set is imputed according to a regression model estimated from an auxiliary data set. However, in some specific settings, a solution may exist. Focusing on a linear outcome regression model with a missing covariate, we show that the bias can be eliminated if the underlying imputation model for the missing covariate is nonlinear in the common variables measured in both data sets. Otherwise, we describe 2 alternative approaches existing in the data fusion literature that could partially resolve this issue: One fits the outcome model by leveraging an additional validation data set containing joint observations of the outcome and the missing covariate, and the other offers informative bounds for the outcome regression coefficients without using validation data. We justify these 3 methods in a linear outcome model and briefly discuss their extension to general settings.
资助项目	National Natural Science Foundation of China[12071015] ; Beijing Natural Science Foundation[Z190001]
WOS研究方向	Public, Environmental & Occupational Health
语种	英语
出版者	OXFORD UNIV PRESS INC
WOS记录号	WOS:000791040600001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/60408]
专题	中国科学院数学与系统科学研究院
通讯作者	Miao, Wang
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China 2.Renmin Univ China, Dept Biostat & Epidemiol, Beijing, Peoples R China 3.Renmin Univ China, Ctr Appl Stat, Beijing, Peoples R China 4.Peking Univ, Sch Math Sci, Dept Probabil & Stat, 5 Summer Palace Rd, Beijing 100871, Peoples R China
推荐引用方式 GB/T 7714	Miao, Wang,Li, Wei,Hu, Wenjie,et al. Invited Commentary: Estimation and Bounds Under Data Fusion Comment[J]. AMERICAN JOURNAL OF EPIDEMIOLOGY,2021:5.
APA	Miao, Wang,Li, Wei,Hu, Wenjie,Wang, Ruoyu,&Geng, Zhi.(2021).Invited Commentary: Estimation and Bounds Under Data Fusion Comment.AMERICAN JOURNAL OF EPIDEMIOLOGY,5.
MLA	Miao, Wang,et al."Invited Commentary: Estimation and Bounds Under Data Fusion Comment".AMERICAN JOURNAL OF EPIDEMIOLOGY (2021):5.