The Impact of Diagnostic Code Misclassification on Optimizing the Experimental Design of Genetic Association Studies. (18th October 2017)

• Record Type: Journal Article
• Authors: Schrodi, Steven J.
• Other Names: Segall Richard Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2017(2017)
• Publication Date: 2017-10-18
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2017/7653071
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : Diagnostic codes within electronic health record systems can vary widely in accuracy. It has been noted that the number of instances of a particular diagnostic code monotonically increases with the accuracy of disease phenotype classification. As a growing number of health system databases become linked with genomic data, it is critically important to understand the effect of this misclassification on the power of genetic association studies. Here, I investigate the impact of this diagnostic code misclassification on the power of genetic association studies with the aim to better inform experimental designs using health informatics data. The trade-off between (i) reduced misclassification rates from utilizing additional instances of a diagnostic code per individual and (ii) the resulting smaller sample size is explored, and general rules are presented to improve experimental designs.