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AMIA Annu Symp Proc. 2017 Feb 10;2016:1764-1773. eCollection 2016.

An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies.

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Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, USA.
School of Public Health, The University of Texas Health Science Center at Houston Houston, TX, USA.
School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.


Over the last decade, Electronic Health Records (EHR) systems have been increasingly implemented at US hospitals. Despite their great potential, the complex and uneven nature of clinical documentation and data quality brings additional challenges for analyzing EHR data. A critical challenge is the information bias due to the measurement errors in outcome and covariates. We conducted empirical studies to quantify the impacts of the information bias on association study. Specifically, we designed our simulation studies based on the characteristics of the Electronic Medical Records and Genomics (eMERGE) Network. Through simulation studies, we quantified the loss of power due to misclassifications in case ascertainment and measurement errors in covariate status extraction, with respect to different levels of misclassification rates, disease prevalence, and covariate frequencies. These empirical findings can inform investigators for better understanding of the potential power loss due to misclassification and measurement errors under a variety of conditions in EHR based association studies.

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