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.