Comparing proportional hazards and accelerated failure time models for survival analysis

2002 ◽  
Vol 21 (22) ◽  
pp. 3493-3510 ◽  
Author(s):  
Jesus Orbe ◽  
Eva Ferreira ◽  
Vicente Núñez-Antón
Keyword(s):  
Survival Analysis ◽  
Proportional Hazards ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

Some Reliability Models for Analyzing the Effect of Operating Conditions

1997 ◽  
Vol 04 (02) ◽  
pp. 133-148 ◽  
Author(s):  
Dhananjay Kumar ◽  
Ulf Westberg
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Failure Time ◽  
Operating Conditions ◽  
Accelerated Failure Time ◽  
Data Set ◽  
Proportional Hazards Models ◽  
Reliability Models ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure

Basic approaches of some of the reliability models available for analyzing the effect of operating conditions (or covariates) on the lifetime of a system are shortly discussed, and a general guideline for how to select an appropriate model for a given data set is provided. Some of the models have theoretical and computational difficulties which make them difficult to apply. Models that appear to be suitable for practical applications can broadly be classified as the class of proportional hazards models and the class of accelerated failure time models. In the class of proportional hazards models, e.g. the proportional hazards model and the proportional odds model, the effect of the covariates is assumed to act multiplicatively on the hazard rate or its transformations. In the class of accelerated failure time models, e.g. the parametric regression models, the effect of the covariates is assumed to act multiplicatively on the failure time or its transformations. Models from the proportional hazards family appear to be the better ones for analyzing the effect of the covariates due to the method used for estimating the parameters of these models.

scholarly journals REGULAR VARIATION AND THE IDENTIFICATION OF GENERALIZED ACCELERATED FAILURE-TIME MODELS

2014 ◽  
Vol 31 (6) ◽  
pp. 1229-1248 ◽  
Author(s):  
Jaap H. Abbring ◽  
Geert Ridder
Keyword(s):  
Proportional Hazards ◽  
Failure Time ◽  
Sufficient Conditions ◽  
Error Distribution ◽  
Accelerated Failure Time ◽  
Necessary And Sufficient Condition ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure ◽  
Necessary And Sufficient ◽  
Economic Studies

Ridder (1990, Review of Economic Studies 57, 167–182) provides an identification result for the Generalized Accelerated Failure-Time (GAFT) model. We point out that Ridder’s proof of this result is incomplete, and provide an amended proof with an additional necessary and sufficient condition that requires that a function varies regularly at 0 and ∞. We also give more readily interpretable sufficient conditions on the tails of the error distribution or the asymptotic behavior of the transformation of the dependent variable. The sufficient conditions are shown to encompass all previous results on the identification of the Mixed Proportional Hazards (MPH) model. Thus, this paper not only clarifies, but also unifies the literature on the nonparametric identification of the GAFT and MPH models.

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.  

Sign in / Sign up

Export Citation Format

Share Document