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An augmented estimation procedure for EHR-based association studies accounting for differential misclassification.

Jiayi Tong1, Jing Huang1, Jessica Chubak2

  • 1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, Pennsylvania, USA.

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|October 17, 2019
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Summary
This summary is machine-generated.

Researchers developed a new method to improve electronic health record (EHR) research by combining error-prone automated data with gold-standard manual reviews. This approach reduces bias and variance in identifying health risk factors.

Keywords:
association studybias reductiondifferential misclassificationelectronic health recordserror in phenotype

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Area of Science:

  • Biomedical Informatics
  • Health Services Research
  • Epidemiology

Background:

  • Electronic health records (EHRs) are valuable for identifying health risk factors.
  • However, errors in EHR-derived phenotypes limit the validity of these studies.
  • Accurate risk factor identification is crucial for improving patient outcomes.

Purpose of the Study:

  • To develop a novel procedure for reducing bias in estimated associations between risk factors and phenotypes using EHR data.
  • To combine the strengths of gold-standard manual reviews with potentially error-prone automated EHR phenotypes.
  • To improve the accuracy of risk factor analyses in large EHR datasets.

Main Methods:

  • Developed an augmented estimator combining a gold-standard phenotype from manual chart review with an algorithm-derived phenotype from EHR data.
  • Utilized a small validation set for the gold-standard phenotype and all available patients for the algorithm-derived phenotype.
  • Conducted simulation studies and applied the method to analyze risk factors for second breast cancer events.

Main Results:

  • The augmented estimator demonstrated lower variance (higher statistical efficiency) compared to using only validation data.
  • The augmented estimator showed smaller bias compared to using only error-prone EHR-derived phenotypes.
  • Effectively balanced bias-variance trade-offs in EHR-based risk factor analysis.

Conclusions:

  • The proposed estimator effectively combines error-prone EHR phenotypes with limited gold-standard data.
  • This method enhances the analysis of risk factors using electronic health record data.
  • Improves statistical efficiency and reduces bias in observational health research.