Invited Commentary: Estimation and Bounds Under Data Fusion Comment
文献类型：期刊论文
| 作者 | Miao, Wang4; Li, Wei2,3; Hu, Wenjie4; Wang, Ruoyu1; Geng, Zhi4 |
| 刊名 | AMERICAN JOURNAL OF EPIDEMIOLOGY
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000791040600001 |
| 出版者 | OXFORD UNIV PRESS INC |
| 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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