Journal Articles (All Issues)

Abstract

Survival analysis is the statistical methodology which is used in case of censored observation. Censored means incomplete information of the study subject. In survival analysis, it is considered that the outcome variable of interest is time until an event occurs. In this study, an attempt has been made to fit Cox Proportional hazard (PH) model and compare the estimated value with Accelerated Failure Time (AFT) models having some probability distributions considering as exponential, weibull, log-normal, log-logistic etc. in the survival data of esophagus cancer patients. After fitting the models by using model selection criterion, the best fitted model is identified. The survival behaviour of esophagus cancer patients are observed by considering various demographic, socio-economic and disease factors by using the best fitted model. A sample of the esophagus cancer with survival data is collected from hospitals records. In survival analysis, the model comparison process which is also known as model selection process is mostly used to find the best fitted model. Some mostly used criteria are Akaike’s Information Criteria (AIC), Bayesian Information Criteria (BIC), Cox-Snell residual plot etc. From the observation it is seen that Weibull AFT model is better fitted than other models. Different factors such as stage at the time of diagnosis, cancer directed treatment taken, socio-economic status of the patients etc. are found to contribute to survival time of esophagus cancer patients. The patients diagnosed in an early stage survive much more than the patients diagnosed at a later stage. The patients undergo cancer directed treatment other than surgery have lower survival time than surgery patients. The death risk is more in patients who are from lower and middle socio-economic group as compared to higher socio-economic group of patients. In this study, though age is not a significant factor in case of esophagus cancer, the patients belonging to older age groups have a higher risk of dying in comparison to the younger age group.

References

[1] Berkson, Joseph and Gage, P.: Survival Curve for Cancer Patients Following Treatment, Journal of the American Statistical Association, 1952, 47 (259 ), 501- 515. [2] Cox, D.R.: Regression Models and Life Tables, Journal of the Royal Statistical Society,Series B, (Methodological), 1972, 34 (2), 187-220. [3] Wei, L.J.: The Accelerated Failure Time Model: A Useful Alternative to The Cox Regression Model In Survival Analysis, Statistics in Medicine, 1992 ,11 (14‐15), 1871-1879. [4] Kalbfleisch, J. D., & Prentice, R. L.: The Statistical Analysis of Failure Time Data, 2nd edition, John Wiley & Sons, 2002, New-York. [5] Orbe,Jesus, F. Eva, & Nunez- Anton, Vicente: Comparing Proportional Hazards and Accelerated Failure Time models for Survival Analysis, Statistics in Medicine, 2002, 21(22), 3493-3510. [6] World Health Organization (WHO). Global Health Estimates 2020: Deaths by Cause, Age, Sex, by Country and by Region, 2000-2019, WHO, 2020. Accessed December 11, 2020. [7] Yang J, Lin X, Cao Sai, Dong X, Rao S, Cai, K.: Understanding Esophageal Cancer: The Challenges and Opportunities for the Next Decade, Frontiers in Oncology, 2020, https://doi.org/10.3389/fonc.2020.01727. [8] American Cancer Society. Facts & Figures, 2022, American Cancer Society, Atlanta, Ga. , 2022. [9] Deepjyoti Kalita, Srinivas Bannoth, Joydeep Purkayastha, Abhijit Talukdar, Gaurav Das, Pritesh Singh : A Study of Hormonal Receptors in Esophageal Carcinoma: Northeast Indian Tertiary Cancer Center Study, South Asian J Cancer, 2021, 9, 222–226. [10] Lawless, J. F.: Statistical Models And Methods For Lifetime Data Analysis,1982,1st edition, Wiley, New York. [11] Saikia, Rinku & Barman, Manash Pratim : A Review on Accelerated Failure Time Models, International Journal of Statistics and Systems, 2017, 12(2), 311-322. [12] Akaike H : A New Look at the Statistical Model Identification, IEEE Transaction and Automatic Control, 1974, 19 (6), 716-23. [13] Schwarz, G.: Estimating the Dimension of a Model, Ann.Statist., 1978, 6(2), 461-464. [14] Cox D.R., and Snell E.J.: A General Definition of Residuals (with discussion), Journal of the Royal Statistical Society, Series B (Methodological), 1968, 30 (2), 248-275. [15] Stute, W.: Consistent Estimation under Random Censorship when Covariables are Present, Journal of Multivariate Analysis, 1993, 45, 89-103. [16] Stute, W.: Distributional Convergence Under Random Censorship when Covariables are Present , Journal of Statistics, 1996, 23, 461- 471. [17] Nardi, Alessandra and Schemper, Michael : Comparing Cox and Parametric models in Clinical Studies, Statistics in Medicine, 2003, 22, 3597-3610. [18] Mandal, Micha & Ritov, Ya'akov : The Accelerated Failure Time Model Under Biased Sampling, Biometrics, 2010, 66 (4), 1306-1308. [19] Ponnuraja, C., & Venkatesan, P.: Survival Models for Exploring Tuberculosis Clinical Trial data-an empirical Comparison, Indian Journal of Science and Technology, 2010, 3(7),755-758. [20] Khanal, Shankhar P., Sreenivas, V., & Acharya, S. K.: Accelerated Failure Time Models: An Application in the Survival of Acute Liver Failure Patients in India, International Journal of Science of Research (IJSR), 2012, 3 (6), 161-166. [21] Tolosie, K., & Sharma, M. K. : Application of Cox Proportional Hazards Model in Case of Tuberculosis Patients in Selected Addis Ababa Health Centres, 2014, Ethiopia, Tuberculosis Research and Treatment . [22] Kargarian-Marvasti, S, Rimaz, S, Ablghasemi J, Heydari I : Comparing of Cox Model and Parametric Models in analysis of Effective Factors on Event Time of Neuropathy in Patients with type 2 diabetes, J Res Med Sci., 2007, 22- 115. [23] Wuryandari, T., Kartiko, S. H., & Danardono : The Cox Proportional Hazard Model on Duration of Birth Process, (Coference presentation), (2018, 17th October). Semarang, Indonesia: Journal of Physics: Conference Series (JPCS). [24] Bustan, M. Nadjib , Kasim Aidid, A.M., Gobel, F. A., & Syamsidar : Cox Proportional Hazard Survival Analysis to Inpatient Breast Cancer Cases, 2nd International Conference on Statistics, Mathematics, Teaching, and Research, Journal Of Physics:Conference Series,(2018, October 9-10) ,1028,Makassar, Indonesia. [25] Faruk Alfensi : The Comparison of Proportional Hazards and Accelerated Failure Time models in Analyzing the First Birth interval Survival Data, Journal of Physics: Conference Series, 2018. [26] Goerdten, Jantje, Carriere Isabelle, Muniz-Terrera, Graciela: Comparison of Coxproportional Hazards Regression and Generalized Cox Regression models Applied in Dementia Risk Prediction, Translational Research Clinical Interventions, 2020. [27] Hassen, Y, Hamid, Teka, A. Mohammed & Addisse, Adamu: Survival Status of Esophageal Cancer Patients and its Determinants in Ethiopia: A Facility Based Retrospective Cohort Study, Frontiers in Oncology, 10, 2021. [28] Chen,Hao-Yun, Chen, Chief, Chen, Yi-Huei, Chen, Chi-Chang, Chuang, Cheng –Yen & Lin, Ching-Heng: The Influence of Socioeconomic Status on Esophageal Cancer in Taiwan: A Population-Based Study, Journal of Personalized Medicine, 12, 2022.