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