DOI QR코드

DOI QR Code

Analyzing Survival Data as Binary Outcomes with Logistic Regression

  • Lim, Jo-Han (Department of Statistics, Seoul National University) ;
  • Lee, Kyeong-Eun (Department of Statistics, Kyungpook National University) ;
  • Hahn, Kyu-S. (Underwood International College, Yonsei University) ;
  • Park, Kun-Woo (Department of Statistics, Seoul National University)
  • Published : 2010.01.31

Abstract

Clinical researchers often analyze survival data as binary outcomes using the logistic regression method. This paper examines the information loss resulting from analyzing survival time as binary outcomes. We first demonstrate that, under the proportional hazard assumption, this binary discretization does result in a significant information loss. Second, when fitting a logistic model to survival time data, researchers inadvertently use the maximal statistic. We implement a numerical study to examine the properties of the reference distribution for this statistic, finally, we show that the logistic regression method can still be a useful tool for analyzing survival data in particular when the proportional hazard assumption is questionable.

Keywords

References

  1. Abbott, R. D. (1985). Logistic regression in survival analysis, American Journal of Epidemiology, 121, 465-471. https://doi.org/10.1093/oxfordjournals.aje.a114019
  2. Annesi, I., Moreau, T. and Lellouch, J. (1989). Efficiency of the logistic regression and cox proportional hazards models in longitudinal studies, Statistics in Medicine, 8, 1515-1521. https://doi.org/10.1002/sim.4780081211
  3. Cain, K. C., Martin, D. P., Holubkov, A. L., Raghunathan, T. E., Cole, W. G. and Thompson, A. (1994). A logistic regression model of mortality following hospital admissions among medicare patients: Comparison with HCFA's model, AHSR FHSR Annual Meeting Abstract Book, 11, 81-82.
  4. Efron, B. (1977). The efficiency of Cox's likelihood function for censored data, Journal of the American Statistical Association, 72, 557-565. https://doi.org/10.2307/2286217
  5. Ingram, D. D. and Kleinman, J. C. (1989). Empirical comparisons of proportional hazards amd logistic reression models, Statistics in Medicine, 8, 525-538. https://doi.org/10.1002/sim.4780080502
  6. Kalbfleisch, J. D. and Prentice, R. (1980). Statistical Analysis of Failure Time Data, Wiley-Interscience, New York.
  7. Moriguchi, S., Hayashi, Y., Nose, Y., Maehara, Y., Korenaga, D. and Sugimachi, K. (2006). A comparison of the logistic regression and the cox proportional hazards models in the retrospective studies on the prognosis of patients with castric cancer, Journal of Surgical Oncology, 52, 9-13.

Cited by

  1. Sepsis in Haiti: Prevalence, treatment, and outcomes in a Port-au-Prince referral hospital: Methodological issues 2017, https://doi.org/10.1016/j.jcrc.2017.06.017