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

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 combi

SUBMITTER: Tong J 

PROVIDER: S-EPMC7025368 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature