IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 3- Issue 11, November 2014 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Survival Analysis By Using Cox Regression Model With Application

[Full Text]

 

AUTHOR(S)

Dr. Monem A. Mohammed

 

KEYWORDS

Index Terms: Cox regression model, survival time, with left-censored data, testing distribution of survival time by using goodness of fit, Kaplan–Meier estimator to estimating the survival function and partial likelihood) method with (Wald) test.

 

ABSTRACT

Abstract: Cox regression model is one of the models can be used in analyzing survival data and we can detect relationship between the explanatory variables and their survival time, so the cox regression is semi parametric model that consist two parts, the first part is nonparametric (λ_0 (t)) and other is parametric part (e^(((β▁z)) ́ )) where ((β)) ́ is the vector of unknown parameters, (▁z) is the vector of explanatory variable. The data which used in this study is type one of censoring was taken from hospital with left-censored data, testing distribution of survival time by using goodness of test and we find the distribution of survival time is unknown. Selecting cox regression model as the best model to analysis data by checking the assumption Cox regression model once graphically by using Kaplan–Meier estimator to estimating the survival function from lifetime data of patients, We estimated the parameters by using (partial likelihood) method and test the model parameter by using (Wald) test which shown that only two parameters(treatment and anemia status) are effect on survival time.

 

REFERENCES

[1] Agresti A.”Categorical Data Analysis”. John Wiley and Sons, New York, 1990.

[2] Bender, ”Generating survival times to simulate cox Proportional hazard models”, sander for schung sbereich,386, p338, 2003.

[3] Cox D.R., ”partial likelihood”, biometric , 62, 2 , p(269-276), 1975.

[4] Inger person ,” Essays on the Assumption of Proportional Hazards in Cox Regression” Uppsala University. Sweden, 2002

[5] Izenman , A.J. and Tran, L.T.,”Estimation of the survival function and hazard rate”, Journal of stat planning and Inference, V .24,p(233-247), 1990.

[6] Long,J.Scott,”Regression Models for categorical and limited dependent variables”, Sage publication, Oaks, 1997.

[7] Nihal Ata and M.Tekin Säozer, ” cox regression model with Nonprortional hazard applied to lung cancer survival”, Hacettepe Journal of Mathematics and Statistics,vol.36, No.2 , p(157-167), 2007.

[8] Walter A. Shewhart and Samuel S. Wilks,”Weibull Models” Johan Wiley & Sons. New York, 2004