Study and Analysis of the Chest Cancer Data Using Survival Models
DOI:
https://doi.org/10.25212/lfu.qzj.4.2.24Keywords:
Survival Analysis, Cox-proportional hazard, Accelerated Failure Time, Kaplan Meier, Log-Rank test, Chest Cancer.Abstract
This research aimed to estimate the effects of prognostic factors on chest cancer survival, the research studied two models in survival analysis; the Cox-Proportional Hazard (PH) model is most usable method in present time of survival data in the occurrence covariate or prognosticates aspects, and the Accelerated Failure Time (AFT) model is another substitute way for analysis of survival data. KaplanMeier method has been applied to survival function and hazard function for estimation, the log-rank test was used to test the differences in the survival analysis. The data was obtained from Nanakali Hospital in the period from 1st January 2013 to 31st December 2017 with follow up period until 1st April 2018. The results for Kaplan-Meier and log-rank test showed the significant difference in survival or death by chest cancer for all presented related prognostic factors. The Cox-PH and AFT model does not identify the same prognostic factors that influenced in chest cancer survival. The Cox Proportional Hazards model displays a significant lack of fit while the accelerated failure time model describes the data well. AFT with Weibull
distribution was chosen to be the best model for our data by using Tow model selection criterion; Akaike Information Criterion (AIC) and Bayesian information criterion (BIC). Also, the results performed by the statistical package in Mat-lab, Stat-graphic and SPSS, which was used to analyze the data.
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References
Abbas, D. N.–D. (2012). Analysis of Breast Cancer Data using Kaplan– Meier Survival Analysis. Journal of Kufa for Mathematics and
Computer, 7-14.
Alhasawi, E. (2015). Survival Analysis Approaches for Prostate Cancer. Sudbury, Ontario, Canada: Laurentian University.
Biost, A. (2004). Introduction to Survival Analysis. new yourk: A Stata Press Publication.
Cleves, M. &. (2010). An Introduction to Survival Analysis Using Stata. Texas: A Stata Press Publication.
Dhillon, B. S. (2000). Design Reliability Fundemential and Application. Ottawa, Ontario, Canada: Library of Congress Cataloging-inPublication Data.
Ekman, A. (2017). Cox Proportional Hazard Model. Umea: John Wiley & Sons, Inc.
Emmanuel, B. K. (2017). Survival Analysis with Interval-Censored Data. new york : Chapman & Hall/Crc.
Heagerty, P. (2005). Survival Analysis. Va/Uw Summer.
Ibrahim, J. G.-H. (2001). Bayesian Survival Analysis. New York: Springer Science+Business Media, LLC.
John, F. (2014). Introduction to Survival analysis. new work: sociology 761.
Moore, D. F. (2016). Applied Survival Analysis Using R. Piscataway, NJ,: © Springer International Publishing Switzerland.
Qi, J. (2009). Comparison of Proportional Hazards and Accelerated Failure Time Models. Saskatchewan, 1-89.
Sulaiman, E. I. (2017). The Kaplan Meier Estimate in Survival Analysis. Biometrics & Biostatistics International Journal, 1-5.
Vittinghoff, E. &. (2004). Statistics for Biology and Health ,Second edition. New York: © Springer Science+Business Media, LLC 2004,
Wienke, A. (2011). Frailty Models in Survival Analysis. london: Chapman & Hall/CRC.
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Copyright (c) 2019 Kurdistan Ibrahim Mawlood, Researcher Sami Ali Obed
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