Table of Contents
International Journal of Quality, Statistics, and Reliability
Volume 2008 (2008), Article ID 471607, 10 pages
http://dx.doi.org/10.1155/2008/471607
Research Article

Sensitivity Analysis to Select the Most Influential Risk Factors in a Logistic Regression Model

School of Mathematical Sciences, University Sains Malaysia, 11800 Penang, Malaysia

Received 1 August 2008; Revised 17 October 2008; Accepted 25 November 2008

Academic Editor: Myong K. (MK) Jeong

Copyright © 2008 Jassim N. Hussain. 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. Saltelli, K. Chan, and E. M. Scott, Sensitivity Analysis, John Wiley & Sons, Chichester, UK, 2000.
  2. A. Saltelli, M. Ratto, S. Tarantola, and F. Campolongo, “Sensitivity analysis for chemical models,” Chemical Reviews, vol. 105, no. 7, pp. 2811–2827, 2005. View at Publisher · View at Google Scholar
  3. A. Khalili and J. Chen, “Variable selection in finite mixture of regression models,” Journal of the American Statistical Association, vol. 102, no. 479, pp. 1025–1038, 2007. View at Publisher · View at Google Scholar
  4. A. J. Miller, Subset Selection in Regression, Chapman & Hall/CRC, London, UK, 2nd edition, 2002.
  5. R. Tibshirani, “The lasso method for variable selection in the Cox model,” Statistics in Medicine, vol. 16, no. 4, pp. 385–395, 1997. View at Publisher · View at Google Scholar
  6. J. Fan and R. Li, “Variable selection for Cox's proportional hazards model and frailty model,” Annals of Statistics, vol. 30, no. 1, pp. 74–99, 2002. View at Publisher · View at Google Scholar
  7. J. Fan and R. Li, “Variable selection via nonconcave penalized likelihood and its oracle properties,” Journal of the American Statistical Association, vol. 96, no. 456, pp. 1348–1360, 2001. View at Publisher · View at Google Scholar
  8. H. H. Zhang and W. Lu, “Adaptive Lasso for Cox's proportional hazards model,” Biometrika, vol. 94, no. 3, pp. 691–703, 2007. View at Publisher · View at Google Scholar
  9. A. Agresti, Categorical Data Analysis, John Wiley & Sons, Hoboken, NJ, USA, 2nd edition, 2002.
  10. D. Collett, Modeling Binary Data, Chapman & Hall/CRC, Boca Raton, Fla, USA, 2nd edition, 2003.
  11. J. Cohen, P. Cohen, S. G. West, and L. S. Alken, Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates, Mahwah, NJ, USA, 3rd edition, 2003.
  12. D. R. Cox and E. J. Snell, Analysis of Binary Data, Chapman & Hall/CRC, New York, NY, USA, 2nd edition, 1989.
  13. T. M. Therneau and P. M. Grambsch, Modeling Survival Data: Extending the Cox Model, Springer, New York, NY, USA, 2000.
  14. A. Saltelli, “Global sensitivity analysis: an introduction,” in Sensitivity Analysis of Model Output, K. M. Hanson and F. M. Hemez, Eds., pp. 27–43, Los Alamos National Laboratory, Los Alamos, NM, USA, 2005. View at Google Scholar
  15. A. Saltelli, S. Tarantola, and K. P.-S. Chan, “A quantitative model-independent method for global sensitivity analysis of model output,” Technometrics, vol. 41, no. 1, pp. 39–56, 1999. View at Publisher · View at Google Scholar
  16. A. Saltelli, S. Tarantola, and F. Campolongo, “Sensitivity analysis as an ingredient of modeling,” Statistical Science, vol. 15, no. 4, pp. 377–395, 2000. View at Publisher · View at Google Scholar
  17. K. Chan, S. Tarantola, A. Saltelli, and I. M. Sobol', “Variance based methods,” in Sensitivity Analysis, A. Saltelli, K. Chan, and M. Scott, Eds., pp. 167–197, John Wiley & Sons, New York, NY, USA, 2000. View at Google Scholar
  18. J. Neter, H. K. Michael, J. N. Christopher, and W. William, Applied Linear Statistical Models, McGraw-Hill, New York, NY, USA, 1996.
  19. J. S. Long, Regression Models for Categorical and Limited Dependent Variables, Sage, Thousand Oaks, Calif, USA, 1997.
  20. M. Saisana, A. Saltelli, and S. Tarantola, “Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators,” Journal of the Royal Statistical Society. Series A, vol. 168, no. 2, pp. 307–323, 2005. View at Publisher · View at Google Scholar
  21. A. Heiat, “Using an Excel extension for selecting the probability distribution of empirical data,” Spreadsheets in Education, vol. 2, no. 1, pp. 95–100, 2005. View at Google Scholar
  22. J. B. Schorling, J. Roach, M. Siegel et al., “A trial of church-based smoking cessation interventions for rural African Americans,” Preventive Medicine, vol. 26, no. 1, pp. 92–101, 1997. View at Publisher · View at Google Scholar
  23. J. I. Cleeman, S. M. Grundy, D. Becker et al., “Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III),” The Journal of the American Medical Association, vol. 285, no. 19, pp. 2486–2497, 2001. View at Publisher · View at Google Scholar
  24. J. T. DiPiro, R. L. Talbert, G. C. Yee, G. R. Matzke, B. G. Wells, and L. M. Posey, Pharmacotherapy: A Pathophysiologic Approach, McGraw-Hill, New York, NY, USA, 6th edition, 2005.
  25. M. A. Koda-Kimble, L. Y. Young, W. A. Kradian, B. J. Guglielmo, B. K. Allderege, and R. L. Corelli, Applied Therapeutics, The Clinical Use of Drugs, Lippincott Williams & Wilkins, Baltimore, Md, USA, 8th edition, 2005.