참고문헌
- Bagley, S. C., White, H., & Golomb, B. A. (2001). Logistic regression in the medical literature: Standards for use and reporting, with particular attention to one medical domain. Journal of Clinical Epidemiology, 54(10), 979-985. https://doi.org/10.1016/S0895-4356(01)00372-9
- Bewick, V., Cheek, L., & Ball, J. (2004). Statistics review 13: Receiver operating characteristic curves. Critical Care (London, England), 8(6), 508- 512. http://dx.doi.org/10.1186/cc3000
- Bewick, V., Cheek, L., & Ball, J. (2005). Statistics review 14: Logistic regression. Critical Care (London, England), 9(1), 112-118. http://dx.doi.org/ 10.1186/cc3045
- Eberhardt, L. L., & Breiwick, J. M. (2012). Models for population growth curves. ISRN Ecology, 2012, 1-7. http://dx.doi.org/doi:10.5402/2012/ 815016
- Giancristofaro, R. A., & Salmaso, L. (2003). Model performance analysis and model validation in logistic regression. Statistica, 63(2), 375-396.
- Harrell, F, E., LEE, K. L., & MARK, D. B. (1996). Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Statistics in Medicine, 15, 361-387. https://doi.org/10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
- Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York, NY: John Wiley & Sons Inc.
- Hsieh, F. Y., Bloch, D. A., & Larsen, M. D. (1998). A simple method of sample size calculation for linear and logistic regression. Statistics in Medicine, 17(14), 1623-1634.
- Katz, M. H. (1999). Multivariable analysis: A practical guide for clinicians. Cambridge: Cambridge University Press.
- Kleinbaum, D. G., & Klein, M. (2010). Logistic regression(statistics for biology and health) (3rd ed.). New York, NY: Springer-Verlag New York Inc.
- Long, J. S. (1997). Regression models for categorical and limited dependent vriables. Thousand Oaks, CA: Sage Publications.
- Menard, S. W. (2001). Applied logistic regression analysis (quantitative applications in the social sciences) (2nd ed.). Thousand Oaks, CA: Sage Publications.
- Morris, J. A., & Gardner, M. J. (1988). Calculating confidence intervals for relative risks (odds ratios) and standardised ratios and rates. British Medical Journal (Clinical Research Ed.), 296(6632), 1313-1316. https://doi.org/10.1136/bmj.296.6632.1313
- Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R. (1996). A simulation study of the number of events per variable in logistic regression analysis. Journal of Clinical Epidemiology, 49(12), 1373-1379. https://doi.org/10.1016/S0895-4356(96)00236-3
- Peng, C. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96(1), 3-14. https://doi.org/10.1080/00220670209598786
- Peng, C. J., & So, T. H. (2002). Logistic regression analysis and reporting: A primer. Understanding Statistics, 1(1), 31-70. https://doi.org/10.1207/S15328031US0101_04
- Tetrault, J. M., Sauler, M., Wells, C. K., & Concato, J. (2008). Reporting of multivariable methods in the medical literature. Journal of Investigative Medicine, 56(7), 954-957. http://dx.doi.org/10.231/JIM.0b013e31818914ff
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