Application of the Misclassification Simulation Extrapolation Procedure to Log-Logistic Accelerated Failure Time Models in Survival Analysis

2018 ◽  
Vol 13 (1) ◽  
Author(s):  
Varadan Sevilimedu ◽  
Lili Yu ◽  
Hani Samawi ◽  
Haresh Rochani
Keyword(s):  
Survival Analysis ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Extrapolation Procedure ◽  
Accelerated Failure Time Models ◽  
Simulation Extrapolation ◽  
Accelerated Failure

scholarly journals Survival analysis of liver cirrhosis patients after transplantation using accelerated failure time models

2018 ◽  
Vol 5 (11) ◽  
pp. 2789-2796 ◽  
Author(s):  
Jamileh Abolghasemi ◽  
Mohsen Nasiri Toosi ◽  
Mahboobeh Rasouli ◽  
Hajar Taslimi
Keyword(s):  
Liver Transplantation ◽  
Liver Cirrhosis ◽  
Survival Analysis ◽  
Blood Cell ◽  
Logistic Model ◽  
Failure Time ◽  
P Value ◽  
Accelerated Failure Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

Introduction: Liver transplantation is known as the only treatment for advanced liver cirrhosis. Considering the importance of identifying the factors affecting the survival of cirrhosis patients after transplantation in order to improve the health of these patients and increase their longevity, this study was conducted to fit the best accelerated failure time model for survival analysis of cirrhosis patients. Methods: This descriptive-analytical study was conducted by collecting the information about 563 patients with liver cirrhosis who underwent liver transplantation in Imam Khomeini Hospital during 2002-2013 and were followed up for at least 5 years. The data were analyzed using Chisquare test, ANOVA, and Kaplan-Meier non-parametric method as well as exponential Accelerated Failure Time, Weibull, Log-Normal, and Log-Logistic survival models. Results: During the study, 92 (16.3%) of the 563 patients under study died and 165 (29.3%) of them suffered liver transplant rejection. The one-year, three-year, and five-year survival of the patients after transplantation was 0.804, 0.653, and 0.420, respectively. Among the fitted Accelerated Failure Time models, the fitted log-logistic model was the most effective (P-value < 0.001). The effective variables in the Multiple regression log-logistic model included bilirubin (P-value < 0.001), INR (P-value < 0.001), creatinine (P-value < 0.001), and white blood cell (P-value = 0.011) logarithms. Conclusion: Regarding the results of the study, bilirubin, INR, creatinine, and white blood cell logarithmic variables were effective in the survival analysis of the patients after liver transplantation. The survival of these patients can be enhanced through necessary care to maximally control these variables.  

scholarly journals Survival analysis of thalassemia major patients using Cox, Gompertz proportional hazard and Weibull accelerated failure time models

2017 ◽  
Vol 31 (1) ◽  
pp. 568-571 ◽  
Author(s):  
Enayatollah Bakhshi ◽  
Reza Ali Akbari Khoei ◽  
Azita Azarkeivan ◽  
Maryam Kooshesh ◽  
Akbar Biglarian
Keyword(s):  
Survival Analysis ◽  
Failure Time ◽  
Thalassemia Major ◽  
Accelerated Failure Time ◽  
Proportional Hazard ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

Structural accelerated failure time models for survival analysis in studies with time-varying treatments

2005 ◽  
Vol 14 (7) ◽  
pp. 477-491 ◽  
Author(s):  
Miguel A. Hernán ◽  
Stephen R. Cole ◽  
Joseph Margolick ◽  
Mardge Cohen ◽  
James M. Robins
Keyword(s):  
Survival Analysis ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Time Varying ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

Effects of covariates on alternating recurrent events in accelerated failure time models

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Moumita Chatterjee ◽  
Sugata Sen Roy
Keyword(s):  
Recurrent Events ◽  
Failure Time ◽  
Likelihood Method ◽  
Model Parameters ◽  
Accelerated Failure Time ◽  
Survival Times ◽  
Copula Functions ◽  
Event Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

AbstractIn this article, we model alternately occurring recurrent events and study the effects of covariates on each of the survival times. This is done through the accelerated failure time models, where we use lagged event times to capture the dependence over both the cycles and the two events. However, since the errors of the two regression models are likely to be correlated, we assume a bivariate error distribution. Since most event time distributions do not readily extend to bivariate forms, we take recourse to copula functions to build up the bivariate distributions from the marginals. The model parameters are then estimated using the maximum likelihood method and the properties of the estimators studied. A data on respiratory disease is used to illustrate the technique. A simulation study is also conducted to check for consistency.

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