PheBC: bias correction methods for EHR derived phenotype

Project Summary
In response to the (PAR-18-896), the overarching goal of this proposal is to fully develop
a joint effort between statisticians, medical informaticians, clinicians with a focus on developing
a rigorous bias correction framework through modern knowledge engineering and data-driven
statistical modeling, for improving the unbiasedness and reproducibility of health system data
driven research.
In this proposal, we will focus on: (1) Develop a novel prior-knowledge-guided integrated
likelihood approach to enable bias correction by incorporating prior phenotyping accuracy. (2)
Develop methods and algorithms to account for EHR phenotyping errors in both outcomes and
predictors. And (3) Validation, Application and Software development. We will use the proposed
bias correction methods to several EHR datasets to replicate existing findings and investigate
new hypothesis in multiple datasets at University of Texas and University of Pennsylvania. We
will also develop software for the proposed methods to facilitate ongoing EHR-based clinical
studies.

Grant Number: 5R01LM013519-04
NIH Institute/Center: NIH
Principal Investigator: Yong Chen