Accelerated Failure Time Models with Applications to Endometrial Cancer Survival Data
DOI:
https://doi.org/10.13052/jrss0974-8024.15213Keywords:
Parametric modeling, AFTM, Cox-PH, endometrial cancer, AIC, BICAbstract
The objective of this study is to determine the significant predictors of endometrial cancer using accelerated failure time models (AFTM). We have demonstrated the applications of AFTM viz. Exponential, Weibull, Log-normal, Log-logistic, Gompertz, Gamma and Generalized Gamma AFTM, as an alternative of Cox proportional hazard model. Data for the analysis was collected from Acharya Harihar Post Graduate Institute of Cancer (AHPGIC), Cuttack, Odisha during the period 2016–20. Based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) value, the Weibull AFTM has been chosen as the best fitted AFT model. The predictors such as age, comorbidity, tumor size, isolated para-aortic and adnexa have been found as significant predictors (p-value < 0.05) to explain the survival of endometrial cancer patients. Hence, by optimizing different treatments, based on such prognostic factors plays an important role in managing endometrial cancer at an early stage.
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Ali, Z. A. R. E., Hosseini, M., Mahmoodi, M., Mohammad, K., Zeraati, H., and Naieni, K. H. (2015). A comparison between accelerated failure-time and Cox proportional hazard models in analyzing the survival of gastric cancer patients. Iranian journal of public health, 44(8), 1095.
Baik, J., and Murthy, D. P. (2008). Reliability assessment based on two-dimensional warranty data and an accelerated failure time model. International Journal of Reliability and Safety, 2(3), 190–208.
Blackburn, B. E., Soisson, S., Rowe, K., Snyder, J., Fraser, A., Deshmukh, V., Newman, M., Smith, K., Herget, K., Kirchhoff, A.C., Kepka, D., Werner, T.L., Gaffney, D., Mooney, K., and Hashibe, M. (2019). Prognostic factors for rural endometrial cancer patients in a population-based cohort. BMC public health, 19(1), 1–9.
Bokoro, P., and Doorsamy, W. (2018). Reliability analysis of low-voltage metal-oxide surge arresters using accelerated failure time model. IEEE Transactions on Power Delivery, 33(6), 3139–3146.
Bregar, A. J., Rauh-Hain, J. A., Spencer, R., Clemmer, J. T., Schorge, J. O., Rice, L. W., and Del Carmen, M. G. (2017). Disparities in receipt of care for high-grade endometrial cancer: a National Cancer Data Base analysis. Gynecologic oncology, 145(1), 114–121.
Chapman, J. A. W., Trudeau, M. E., Pritchard, K. I., Sawka, C. A., Mobbs, B. G., Hanna, W. M., … and Lickley, L. A. (1992). A comparison of all-subset Cox and accelerated failure time models with Cox step-wise regression for node-positive breast cancer. Breast cancer research and treatment, 22(3), 263–272.
Chen, H. H., Ting, W. H., Sun, H. D., Wei, M. C., Lin, H. H., and Hsiao, S. M. (2020). Predictors of Survival in Women with High-Risk Endometrial Cancer and Comparisons of Sandwich versus Concurrent Adjuvant Chemotherapy and Radiotherapy. International Journal of Environmental Research and Public Health, 17(16), 5941.
Coll de la Rubia, E., Martinez-Garcia, E., Dittmar, G., Gil-Moreno, A., Cabrera, S., and Colas, E. (2020). Prognostic biomarkers in endometrial cancer: a systematic review and meta-analysis. Journal of clinical medicine, 9(6), 1900.
Collett, D. (2015). Modelling survival data in medical research.3rd edition, London, UK, Chapman-Hall, CRC press.
Cook, L. S., Kmet, L. M., Magliocco, A. M., and Weiss, N. S. (2006). Endometrial cancer survival among US black and white women by birth cohort. Epidemiology, 17(4), 469–472.
Cox, D. R., and Snell, E. J. (1968). A general definition of residuals (with discussion). Journal of the Royal Statistical Society.
Dessai, S. B., Adrash, D., Geetha, M., Arvind, S., Bipin, J., Nayanar, S., Sachin, K., Biji, M.S., and Balasubramanian, S. (2016). Pattern of care in operable endometrial cancer treated at a rural-based tertiary care cancer center. Indian journal of cancer, 53(3), 416.
Elit, L., Lytwyn, A., and Akhtar-Danesh, N. (2012). Long-Term Trends in the Survival of Women with Endometrial Cancer in Canada: A Population-Based Study. Journal of Cancer Therapy, 3(05), 853.
Goodman, C. R., Hatoum, S., Seagle, B. L. L., Donnelly, E. D., Barber, E. L., Shahabi, S. & Strauss, J. B. (2019). Association of chemotherapy and radiotherapy sequence with overall survival in locoregionally advanced endometrial cancer. Gynecologic oncology, 153(1), 41–48.
Juhan, N., Abd Razak, N., Zubairi, Y. Z., Naing, N. N., Hussin, C. H. C., and Daud, M. A. (2016). Comparison of stratified Weibull model and Weibull accelerated failure time (aft) model in the analysis of cervical cancer survival. Jurnal Teknologi, 78(6–4).
Kay, R., and Kinnersley, N. (2002). 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. Drug information journal, 36(3), 571–579.
Khanal, S. P., Sreenivas, V., and Acharya, S. K. (2014). Accelerated failure time models: an application in the survival of acute liver failure patients in India. Int J Sci Res, 3, 161–166.
Klein, J. P., and Moeschberger, M. L. (1997). Survival Analysis: Techniques for Censored and Truncated Data. 1st Edition, Springer, New York.
Lawless, J. F. (1982). Statistical models and methods for lifetime data, New Work. John Wiley.
Lee, E. T., and Wang, J. (2003). Statistical methods for survival data analysis (Vol. 476). John Wiley & Sons.
Miller, K. D., Siegel, R. L., Lin, C. C., Mariotto, A. B., Kramer, J. L., Rowland, J. H., Stein, K.D., Alteri, R., and Jemal, A. (2016). Cancer treatment and survivorship statistics, 2016. CA: a cancer journal for clinicians, 66(4), 271–289.
Nicholas, Z., Hu, N., Ying, J., Soisson, P., Dodson, M., and Gaffney, D. K. (2014). Impact of comorbid conditions on survival in endometrial cancer. American journal of clinical oncology, 37(2), 131–134.
Orbe, J., Ferreira, E., and Nunez-Anton, V. (2002). Comparing proportional hazards and accelerated failure time models for survival analysis. Statistics in medicine, 21(22), 3493–3510.
Saikia, R., and Barman, M. P. (2017). Comparing accelerated failure time models with its specific distributions in the analysis of esophagus cancer patients’ data. International Journal of Computational and Applied Mathematics, 12(2), 411–424.
Sayehmiri, K., Eshraghian, M. R., Mohammad, K., Alimoghaddam, K., Foroushani, A. R., Zeraati, H. and Ghavamzadeh, A. (2008). Prognostic factors of survival time after hematopoietic stem cell transplant in acute lymphoblastic leukemia patients: Cox proportional hazard versus accelerated failure time models. Journal of Experimental & Clinical Cancer Research, 27(1), 1–9.
Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 461–464.
Solmaz, U., Ekin, A., Mat, E., Gezer, C., Dogan, A., Biler, A. and Sanci, M. (2016). Analysis of clinical and pathological characteristics, treatment methods, survival, and prognosis of uterine papillary serous carcinoma. Tumori Journal, 102(6), 593–599.
Srividhya, K., and Radhika, A. (2019). Comparison of Different Parametric Modeling for Time-to-Event Data Among Cancer Patients. International Journal of Scientific Research in Mathematical and Statistical Sciences, 6(1), 187–192.
Swain, P. K., and Grover, G. (2016). Determination of predictors associated with HIV/AIDS patients on ART using accelerated failure time model for interval censored survival data. Am J Biostat, 6, 12–19.
Wei, L. J. (1992). The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. Statistics in medicine, 11(14–15), 1871–1879.
