An augmented estimation procedure for EHR-based association studies accounting for differential misclassification. (16th October 2019)

• Record Type: Journal Article
• Authors: Tong, Jiayi; Huang, Jing; Chubak, Jessica; Wang, Xuan; Moore, Jason H; Hubbard, Rebecca A; Chen, Yong
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 2(2020)
• Page Start: 244
• Page End: 253
• Publication Date: 2019-10-16
• Subjects: association study -- bias reduction -- differential misclassification -- electronic health records -- error in phenotype; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz180
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objectives: 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. Materials and Methods: The proposed method combines the strengths of a gold-standard phenotype obtained through manual chart review for a small validation set of patients and an automatically-derived phenotype that is available for all patients but is potentially error-prone (hereafter referred to as the algorithm-derived phenotype). An augmented estimator of associations is obtained by optimally combining these 2 phenotypes. We conducted simulation studies to evaluate the performance of the augmented estimator and conducted an analysis of risk factors for second breast cancer events using data on a cohort from Kaiser Permanente Washington. Results: The proposed method was shown to reduce bias relative to an estimator using only the algorithm-derived phenotype and reduce variance compared to an estimator using only the validation data. Discussion: Our simulation studies and real data application demonstrate that, compared to the estimator using validation data only, the augmented estimator has lower variance (ie, higher statistical efficiency). Compared to the estimator using error-prone EHR-derived phenotypes, the augmented estimator has smaller bias. Conclusions: The proposed estimator can effectively combine an error-prone phenotype with gold-standard data from a limited chart review in order to improve analyses of risk factors using EHR data.