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Journal of Healthcare Engineering
Volume 2017 (2017), Article ID 7653071, 5 pages
https://doi.org/10.1155/2017/7653071
Research Article

The Impact of Diagnostic Code Misclassification on Optimizing the Experimental Design of Genetic Association Studies

1Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI, USA
2Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI, USA

Correspondence should be addressed to Steven J. Schrodi; ude.nilcdlfm.frcm@nevets.idorhcs

Received 17 May 2017; Accepted 13 September 2017; Published 18 October 2017

Academic Editor: Richard Segall

Copyright © 2017 Steven J. Schrodi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. A. McCarty, B. N. Mukesh, P. F. Giampietro, and R. A. Wilke, “Healthy people 2010 disease prevalence in the Marshfield Clinic Personalized Medicine Research Project cohort: opportunities for public health genomic research,” Personalized Medicine, vol. 4, no. 2, pp. 183–190, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Ko, M. Cho, J.-S. Lee, and J. Kim, “Identification of disease comorbidity through hidden molecular mechanisms,” Scientific Reports, vol. 6, article 39433, 2016. View at Publisher · View at Google Scholar · View at Scopus
  3. M. R. Boland, Z. Shahn, D. Madigan, G. Hripcsak, and N. P. Tatonetti, “Birth month affects lifetime disease risk: a phenome-wide method,” Journal of the American Medical Informatics Association, vol. 22, no. 5, pp. 1042–1053, 2015. View at Publisher · View at Google Scholar · View at Scopus
  4. L. G. Glance, T. M. Osler, D. B. Mukamel, W. Meredith, J. Wagner, and A. W. Dick, “TMPM-ICD9: a trauma mortality prediction model based on ICD-9-CM codes,” Annals of Surgery, vol. 249, no. 6, pp. 1032–1039, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. J. M. Kinge, K. Saelensminde, J. Dieleman, S. E. Vollset, and O. F. Norheim, “Economic losses and burden of disease by medical conditions in Norway,” Heath Policy, vol. 121, 2017. View at Publisher · View at Google Scholar
  6. S. E. O’Brien, S. J. Schrodi, Z. Ye, M. H. Brilliant, S. S. Virani, and A. Brautbar, “Differential lipid response to statins is associated with variants in the BUD13-APOA5 gene region,” Journal of Cardiovascular Pharmacology, vol. 66, no. 2, pp. 183–188, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Icen, C. S. Crowson, M. T. McEvoy, S. E. Gabriel, and H. Maradit Kremers, “Potential misclassification of patients with psoriasis in electronic databases,” Journal of the American Academy of Dermatology, vol. 59, no. 6, pp. 981–985, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. J. M. Evans and T. M. MacDonald, “Misclassification and selection bias in case-control studies using an automated database,” Pharmacoepidemiology and Drug Safety, vol. 6, no. 5, pp. 313–318, 1997. View at Publisher · View at Google Scholar
  9. J. A. Singh, A. R. Holmgren, and S. Noorbaloochi, “Accuracy of veterans administration databases for a diagnosis of rheumatoid arthritis,” Arthritis and Rheumatism, vol. 51, pp. 952–957, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. W. Birman-Deych, A. D. Waterman, Y. Yan, D. S. Nilasena, M. J. Radford, and B. F. Gage, “Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors,” Medical Care, vol. 43, no. 5, pp. 480–485, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. B. J. Edwards, C. Haynes, M. A. Levenstien, S. J. Finch, and D. Gordon, “Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies,” BMC Genetics, vol. 6, p. 18, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. F. Ji, Y. Yang, C. Haynes, S. J. Finch, and D. Gordon, “Computing asymptotic power and sample size for case-control genetic association studies in the presence of phenotype and/or genotype misclassification errors,” Statistical Applications in Genetics and Molecular Biology, vol. 4, article 37, 2005. View at Publisher · View at Google Scholar
  13. D. Gordon, C. Haynes, Y. Yang, P. L. Kramer, and S. J. Finch, “Linear trend tests for case–control genetic association that incorporate random phenotype and genotype misclassification error,” Genetic Epidemiology, vol. 31, no. 8, pp. 853–870, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Manchia, J. Cullis, G. Turecki, G. A. Rouleau, R. Uher, and M. Alda, “The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases,” PLoS One, vol. 8, no. 10, article e76295, 2013. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Duan, M. Cao, Y. Wu et al., “An empirical study for impacts of measurement errors on EHR based association studies,” American Medical Informatics Association Annual Symposium Proceedings, vol. 2016, pp. 1764–1773, 2017. View at Google Scholar
  16. J. J. Bazarian, P. Veazie, S. Mookerjee, and E. B. Lerner, “Accuracy of mild traumatic brain injury case ascertainment using ICD-9 codes,” Academic Emergency Medicine, vol. 13, no. 1, pp. 31–38, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. S. J. Hebbring, S. J. Schrodi, Z. Ye, Z. Zhou, D. Page, and M. H. Brilliant, “A PheWAS approach in studying HLA-DRB1*1501,” Genes and Immunity, vol. 14, no. 3, pp. 187–191, 2013. View at Publisher · View at Google Scholar · View at Scopus
  18. J. B. Leader, S. A. Pendergrass, A. Verma et al., “Contrasting association results between existing PheWAS phenotype definition methods and five validated electronic phenotypes,” American Medical Informatics Association Annual Symposium Proceedings, vol. 2015, pp. 824–832, 2015. View at Google Scholar