PARAMETRIC REGRESSION MODELING OF COMPETING RISK USING CARDIOVASCULAR DISEASE PATIENT’S SURVIVAL DATA
Keywords:
competing risk, exponential distribution, Weibull distribution, generalized gamma distribution, gamma distribution, AIC, BIC.DOI:
https://doi.org/10.17654/0973514322027Abstract
In the field of survival or medical studies, it was more interesting to examine death (mortality) due to different reasons of failures (competing events) than combining all the causes whereas the occurrence of one of the outcomes of interest stops the occurrence of other outcomes of interest are called competing risks. In this paper, we made an attempt on competing risk model in the presence of covariates whereas the cause of risks assumes to follow some lifetime distributions like exponential, Weibull, Gamma, generalized Gamma. Also, we assume that competing events are uncorrelated and that each subject can experience only one type of event at any particular time. Maximum likelihood estimation procedure is used to estimate the parameters in the parametric models. The results are compared with the proposed models. Validity of the model is assessed using –2LL, AIC, BIC.
Received: July 21, 2022
Accepted: September 28, 2022
References
E. L. Kaplan and P. Meier, Nonparametric estimation from incomplete observations, J. Amer. Statist. Assoc. 53 (1958), 457-481.
C. Park, Parameter estimation of incomplete data in competing risks using the EM algorithm, IEEE Transactions on Reliability 54(2) (2005), 282-290.
A. M. Sarhan, Analysis of incomplete, censored data in competing risks models with generalized exponential distributions, IEEE Transactions on Reliability 56(1) (2007), 132-138.
D. R. Cox, The analysis of exponentially distributed life-times with two types of failures, J. Roy. Statist. Soc. Ser. B 21 (1959), 411-421.
R. L. Prentice, J. D. Kalbfleisch and A. Peterson, Jr., The analysis of failure times in the presence of competing risks, Biometrics 34 (1978), 541-554.
S. Valarmathi and C Ponnuraja, Competing risk Cox proportional hazard model through cause-specific and sub-distributional hazards: a model comparison, International Journal of Advanced Research 3(6) (2015), 583-588.
Zhongheng Zhang, Survival analysis in the presence of competing risk, Annals of Translational Medicine 5(3) (2017), 47.
D. Kundu and S. Basu, Analysis of incomplete data in presence of competing risks, J. Statist. Plann. Inference 87(2) (2000), 221-239.
Ibrahim A. Alwasel, Statistical inference of a competing risks model with modified Weibull distributions, International Journal of Mathematics Analysis, 3(19) (2009), 905-918.
Soraya Moamer, Ahmad Reza Baghestani and Mohamad Amin Pourhoseingholi, Ali Akbar Khaden Maboudi, Soodeh Shahsavari, Mohammad Reza Zali and Tahereh Mohammadi Majd, Application of the parametric regression model with the four-parameter log-logistic distribution for determining of the effecting factors on the survival rate of colorectal cancer patients in the presence of competing risks, Iranian Red Crescent Medical Journal 19 (2017), e55609.
S. Wahed Abdus, The Minh Luong and Jong-Hyeon Jeong, A new generalization of Weibull distribution with application to a breast cancer data set, Stat. Med. 28 (2009), 2077-2094.
J. Mazucheli and J. A. Achcar, The Lindley distribution applied to competing risks lifetime data, Computer Methods and Programs in Biomedicine 104(2) (2011), 188-192.
Soraya Moamer, Ahmad Reza Baghestani and Mohamad Amin Pourhoseingholi, Regression modeling of competing risks survival data in the presence of covariates based on a generalized Weibull distribution: a simulation study, Pak. J. Stat. Oper. Res. 14(2) (2018), 433-445.
Wang Yan, Shi Yimin and Wu Min, Statistical inference for dependence competing risks model under middle censoring, Journal of Systems Engineering and Electronics 30(1) (2019), 209-222.
Ammar M. Sarhan, David C. Hamilton and B. Smith, Statistical analysis of competing risks models, Reliability Engineering and System Safety 95 (2010), 953-962.
Yosra Yousif, Faiz A. M. Elfaki and Meftah Hrairi, Regression analysis of masked competing risks data under cumulative incidence function framework, Austrian Journal of Statistics 49 (2020), 25-29.
Tahani A. Abushal, A. Soliman and G. A. Abd-Elmougod, Statistical inferences of Burr XII lifetime models under joint Type-1 competing risks samples, J. Math. 2021, Article ID 9553617, 16 pp.
Tahani A. Abushal, A. Soliman and G. A. Abd-Elmougod, Inference of partially observed causes for failure of Lomax competing risks model under type-II generalized hybrid censoring scheme, Alexandria Engineering Journal 61 (2022), 5427-5439.
Rakesh Rajan, Sonam Singh, K. Satyanshu and A. Upadhyay, A Bayes analysis of a competing risk model based on gamma and exponential failures, Reliability Engineering and System Safety 144 (2015), 35-44.
E. M. Stacy and G. A. Mihram, Parametric estimation for a generalized gamma distribution, Technometrics 7 (2012), 349-358.
Elisa T. Lee and John Wenyu Wang, Statistical Methods for Survival Data Analysis, John Wiley and Sons, Canada, 2003.
J. F. Lawless, Statistical Models and Methods for Lifetime Data, John Wiley and Sons, New York, 2003.
J. H. Jeong and J. P. Fine, Parametric regression on cumulative incidence function, Biostatistics 8 (2007), 184-196.
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