Application of the accelerated failure time model to lung cancer data

Document Type : Research Paper


College of Administration and EconomicsnUniversity of Kirkuk


Accelerated failure time model sometimes symbolized as AFT model, is an important  regression model in survival analysis. In this article, we applied AFT model to the data of lung cancer patient in order to identify the must important factors affecting the patient's survival time. The results showed a well performance for this model, as based on some statistical criteria, the factors  that are consistent with the opinion of specialists in in uencing survival time were identified, as the  factors (smoking, treatment, proliferation, location of residence) of the main factors a ecting the life of a person with this disease.


[1] H. Akaike, A new look at the statistical model identification, IEEE. Trans. Aut. Cont. 19(6) (1974) 716–723.
[2] M. Cleves, W. W. Gould, R. Gutierrez and Y. Marchenko, An Introduction to Survival Analysis using Stata, Stata Press, 2008.
[3] C. Daviad, Modelling Survival Data in Medical Research, CRC Press, Third Edition, New York, 2015.
[4] A. Faruk, The comparison of proportional hazards and accelerated failure time models in the analysis of the first birth interval survival data, J. Phys. Conf. Ser. 974(1) (2018).
[5] J. Huang, S. Ma and H. Xie, Regularized estimation in the accelerated failure time model with high-dimensional covariates, Biomet. 62(3) (2006) 813–820.
[6] R. Kay and N. Kinnersley, On the use of the accelerated failure time model as an alternative to the proportional hazards model in the treatment of time to event data a case study in influenza, Drud Inf. J. 36(3) (2002) 571–579.
[7] B. Kestenbaum, Epidemiology and Biostatistics, An introduction to Clinical Research, Second Edition, 2019.
[8] S. P. Khanal, V. Sreenivas and S. K. Acharya, Accelerated failure time models: an application in the survival of acute live failure patients in India, Int. J. Sci. Res. 3 (2014) 161–66.
[9] J. Orbe, E. Ferreira and V. Nunez-Anton, Comparing proportional hazards and accelerated failure time models for survival analysis, Stat. Med. 21(22) (2002) 3493–3510.
[10] W. Pan, Using frailties in accelerated failure time model, Lifetime Data Anal. 7(1) (2001) 55–64.
[11] G. Schwarz, Estimating the dimension of a model, Ann. Stat. 6 (1978) 461–464.
[12] S. Walker and B. K. Mallick, A Bayesian semiparametric accelerated failure time model, Biomet. 55(2) (1999) 477–483.
[13] L. J. Wei, The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis, Stat. Med. 11(14-15) (1992) 1871–1879.
[14] A. Wienke, Frailty Models in Survival Analysis, CRC Press, 2010.
[15] K. Yamaguchi, Accelerated failure-time regression models analysis of permanent employment in Japan, J. Amer. Stat. Assoc. 87(418) (1992) 284–292.
[16] D. Zeng and D. Y. Lin, Efficient estimation for the accelerated failure time model, J. Amer. Stat. Assoc. 102(480) (2007) 1387–1396.
Volume 12, Issue 1
May 2021
Pages 1243-1250
  • Receive Date: 09 January 2021
  • Revise Date: 20 February 2021
  • Accept Date: 05 March 2021