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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 821643, 11 pages
http://dx.doi.org/10.1155/2012/821643
Research Article

Application of Seemingly Unrelated Regression in Medical Data with Intermittently Observed Time-Dependent Covariates

1Department of Epidemiology, School of Health & Nutrition, Shiraz University of Medical Sciences, Shiraz 7153675541, Iran
2Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran
3Department of Internal Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz 7134845794, Iran

Received 17 June 2012; Revised 17 October 2012; Accepted 31 October 2012

Academic Editor: Guang Wu

Copyright © 2012 Sareh Keshavarzi 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. A. M. Gad and A. S. Ahmed, “Sensitivity analysis of longitudinal data with intermittent missing values,” Statistical Methodology, vol. 4, no. 2, pp. 217–226, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. C. L. Faucett, N. Schenker, and R. M. Elashoff, “Analysis of censored survival data with intermittently observed time-dependent binary covariates,” Journal of the American Statistical Association, vol. 93, no. 442, pp. 427–437, 1998. View at Google Scholar · View at Scopus
  3. H. Lin, C. E. McCulloch, and R. A. Rosenheck, “Latent pattern mixture models for informative intermittent missing data in longitudinal studies,” Biometrics, vol. 60, no. 2, pp. 295–305, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. B. D. Tom and V. T. Farewell, “Intermittent observation of time-dependent explanatory variables: a multi state approach,” Statistics in Medicine, No, vol. 30, no. 30, pp. 3520–3531, 2011. View at Publisher · View at Google Scholar
  5. C. E. Kennedy and J. P. Turley, “Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU,” Theoretical Biology and Medical Modeling, vol. 8, article 40, 2011. View at Publisher · View at Google Scholar
  6. G. D'Angelo and L. Weissfeld, “Covariates missing by design: comparison of the efficient score to other weighted methods,” Statistics in Medicine, vol. 26, no. 10, pp. 2137–2153, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Zhao, J. F. Lawless, and D. L. McLeish, “Likelihood methods for regression models with expensive variables missing by design,” Biometrical Journal, vol. 51, no. 1, pp. 123–136, 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Lin, L. Katsovich, M. Ghebremichael et al., “Psychosocial stress predicts future symptom severities in children and adolescents with Tourette syndrome and/or obsessive-compulsive disorder,” Journal of Child Psychology and Psychiatry and Allied Disciplines, vol. 48, no. 2, pp. 157–166, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Lin, K. A. Williams, L. Katsovich et al., “Streptococcal upper respiratory tract infections and psychosocial stress predict future tic and obsessive-compulsive symptom severity in children and adolescents with tourette syndrome and obsessive-compulsive disorder,” Biological Psychiatry, vol. 67, no. 7, pp. 684–691, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. J. Roy and X. Lin, “Missing covariates in longitudinal data with informative dropouts: bias analysis and inference,” Biometrics, vol. 61, no. 3, pp. 837–846, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. D. Hedeker and R. D. Gibbons, Longitudinal Data Analysis, John Wiley & Sons, New York, NY, USA, 2006.
  12. J. Roy and X. Lin, “Analysis of multivariate longitudinal outcomes with nonignorable dropouts and missing covariates: changes in methadone treatment practices,” Journal of the American Statistical Association, vol. 97, no. 457, pp. 40–52, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. A. M. Wood, I. R. White, M. Hillsdon, and J. Carpenter, “Comparison of imputation and modelling methods in the analysis of a physical activity trial with missing outcomes,” International Journal of Epidemiology, vol. 34, no. 1, pp. 89–99, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. N. J. Horton and K. P. Kleinman, “Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models,” American Statistician, vol. 61, no. 1, pp. 79–90, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. M. Hu and T. Zhou, “Analysis of missing mechanism in IVUS imaging clinical trials with missing covariates,” Journal of Biopharmaceutical Statistics, vol. 21, no. 2, pp. 282–293, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. R. Makowsky, T. M. Beasley, G. L. Gadbury, J. M. Albert, R. E. Kennedy, and D. B. Allison, “Validity and power of missing data imputation for extreme sampling and terminal measures designs in mediation analysis,” Frontiers in Genetics, vol. 2, article 75, 2011. View at Publisher · View at Google Scholar
  17. R. I. Jennrich and M. D. Schluchter, “Unbalanced repeated-measures models with structured covariance matrices,” Biometrics, vol. 42, no. 4, pp. 805–820, 1986. View at Google Scholar · View at Scopus
  18. A. Zellner, “An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias,” Journal of the American Statistical Association, vol. 57, no. 298, pp. 348–368, 1962. View at Publisher · View at Google Scholar
  19. T. Park and R. F. Woolson, “Generalized multivariate models for longitudinal data,” Communications in Statistics—Simulation and Computation, vol. 21, no. 4, pp. 925–946, 1992. View at Publisher · View at Google Scholar
  20. G. Verbeke and G. Molenberghs, Linear Mixed Models for Longitudinal Data, Springer, New York, NY, USA, 2000.
  21. J. M. Wooldridge, Econometric Analysis of Cross Section and Panel Data, MIT Press, Boston, Mass, USA, 2002.
  22. N. Reisby, L. Gram, P. Bech et al., “Imipramine: clinical effects and pharmacokinetic variability,” Psychopharmacology, vol. 54, no. 3, pp. 263–272, 1977. View at Google Scholar · View at Scopus
  23. D. Hedeker, R. D. Gibbons, C. Waternaux, and J. M. Davis, “Investigating drug plasma levels and clinical response using random regression models,” Psychopharmacology Bulletin, vol. 25, no. 2, pp. 227–231, 1989. View at Google Scholar · View at Scopus
  24. N. H. Timm, Applied Multivariate Analysis, Springer, New York, NY, USA, 2002.
  25. J. L. Schafer and J. W. Graham, “Missing data: our view of the state of the art,” Psychological Methods, vol. 7, no. 2, pp. 147–177, 2002. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Sinharay, H. S. Stern, and D. Russell, “The use of multiple imputation for the analysis of missing data,” Psychological Methods, vol. 6, no. 3, pp. 317–329, 2001. View at Google Scholar · View at Scopus
  27. P. M. Fortin, K. Bassett, and V. M. Musini, “Human albumin for intradialytic hypotension in haemodialysis patients,” Cochrane Database of Systematic Reviews, no. 11, Article ID CD006758, 2010. View at Google Scholar · View at Scopus
  28. M. M. Elsharkawy, A. M. Youssef, and M. Y. Zayoon, “Intradialytic changes of serum magnesium and their relation to hypotensive episodes in hemodialysis patients on different dialysates,” Hemodialysis International, vol. 10, no. 2, pp. S16–S23, 2006. View at Publisher · View at Google Scholar · View at Scopus
  29. H. I. Patel, “Analysis of repeated measures designs with changing covariates in clinical trials,” Biometrika, vol. 73, no. 3, pp. 707–715, 1986. View at Publisher · View at Google Scholar · View at Scopus
  30. A. P. Verbyla and W. N. Venables, “An extension of the growth curve model,” Biometrika, vol. 75, no. 1, pp. 129–138, 1988. View at Publisher · View at Google Scholar · View at Scopus