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Activity Number: 273 - How Advanced Analytic Tools Deliver Insights for Clinical Investigations Through Real World Data
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #300517
Title: Incorporating Prior Knowledge on Phenotyping Accuracy for Association Studies Using Electronic Health Records Data
Author(s): Yong Chen* and Jing Huang
Companies: University of Pennsylvania and University of Pennsylvania
Keywords: Electronic Health Records; phenotyping error; reproducibility; misclassification; bias reduction

Electronic Health Records (EHR) have emerged as a major source of data for clinical and health services research. Despite great potential, the complex and inconsistent nature of EHR data brings additional challenges for many clinical studies. A key challenge is the EHR data errors resulting from phenotype misclassification. Inappropriate handling of such errors may lead to reproducibility of findings across studies, which raises a fundamental concern about the value of these researching findings. In this talk, we will present novel methods that can conduct association analysis with well controlled Type I error and competing statistical power by accounting for complex differential misclassification mechanisms. We will also present several case studies of the proposed method, compared to existing methods.

Authors who are presenting talks have a * after their name.

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