Table of Contents
Epidemiology Research International
Volume 2013 (2013), Article ID 875234, 6 pages
http://dx.doi.org/10.1155/2013/875234
Research Article

First Use of Multiple Imputation with the National Tuberculosis Surveillance System

1Division of Infectious Diseases & HIV Medicine, Drexel University College of Medicine, 245 N 15th Street MS 461, New College Building 6314, Philadelphia, PA 19102, USA
2Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
3Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
4Division of Tuberculosis Elimination, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA

Received 27 July 2012; Accepted 18 December 2012

Academic Editor: Huibert Burger

Copyright © 2013 Christopher Vinnard et al. 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. J. W. Graham, “Missing data analysis: making it work in the real world,” Annual Review of Psychology, vol. 60, pp. 549–576, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. A. R. T. Donders, G. J. M. G. van der Heijden, T. Stijnen, and K. G. M. Moons, “Review: a gentle introduction to imputation of missing values,” Journal of Clinical Epidemiology, vol. 59, no. 10, pp. 1087–1091, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. D. B. Rubin, Multiple Imputation For Nonresponse in Surveys, John Wiley & Sons, New York, NY, USA, 1987.
  4. J. L. Schafer, Analysis of Incomplete Multivariate Data, Chapman & Hall, New York, NY, USA, 1997.
  5. J. L. Schafer, “Multiple imputation: a primer,” Statistical Methods in Medical Research, vol. 8, no. 1, pp. 3–15, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Vinnard, C. A. Winston, E. P. Wileyto, R. R. MacGregor, and G. P. Bisson, “Isoniazid resistance and death in patients with tuberculous meningitis: retrospective cohort study,” British Medical Journal, vol. 341, no. 7773, p. 596, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. “Trends in tuberculosis—United States, 2008,” Morbidity and Mortality Weekly Report (MMWR), vol. 58, pp. 249–253, 2009.
  8. S. van Buuren, “Multiple imputation of discrete and continuous data by fully conditional specification,” Statistical Methods in Medical Research, vol. 16, no. 3, pp. 219–242, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. K. J. Lee and J. B. Carlin, “Multiple imputation for missing data: fully conditional specification versus multivariate normal imputation,” American Journal of Epidemiology, vol. 171, no. 5, pp. 624–632, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Royston, “Multiple imputation of missing values: further update of ice, with an emphasis on categorical variables,” The Stata Journal, vol. 9, no. 3, pp. 466–477, 2009. View at Google Scholar · View at Scopus
  11. K. G. Moons, R. A. Donders, T. Stijnen, and F. E. Harrell Jr., “Using the outcome for imputation of missing predictor values was preferred,” Journal of Clinical Epidemiology, vol. 59, pp. 1092–1101, 2006. View at Google Scholar
  12. P. Royston, J. B. Carlin, and I. R. White, “Multiple imputation of missing values: new features for mim,” The Stata Journal, vol. 9, no. 2, pp. 252–264, 2009. View at Google Scholar · View at Scopus
  13. M. A. Klebanoff and S. R. Cole, “Use of multiple imputation in the epidemiologic literature,” American Journal of Epidemiology, vol. 168, no. 4, pp. 355–357, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. F. E. Harrell Jr., Regression Modeling Strategies: With Applications To Linear Models, Logistic Regression, and Survival Analysis, Springer, New York, NY, USA, 2001.