Open AccessAn augmented estimation procedure for EHR-based association studies accounting for differential misclassification
Author(s) -
Jiayi Tong,
Jing Huang,
Jessica Chubak,
Xuan Wang,
Jason H. Moore,
Rebecca A. Hubbard,
Yong Chen
Publication year - 2019
Publication title -
journal of the american medical informatics association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.614
H-Index - 150
eISSN - 1527-974X
pISSN - 1067-5027
DOI - 10.1093/jamia/ocz180
Subject(s) - estimator , computer science , standard error , statistics , variance (accounting) , data mining , gold standard (test) , small area estimation , mathematics , business , accounting
The ability to identify novel risk factors for health outcomes is a key strength of electronic health record (EHR)-based research. However, the validity of such studies is limited by error in EHR-derived phenotypes. The objective of this study was to develop a novel procedure for reducing bias in estimated associations between risk factors and phenotypes in EHR data.
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