scholarly journals Two-Sample Statistics for Testing the Equality of Survival Functions Against Improper Semi-parametric Accelerated Failure Time Alternatives: An Application to the Analysis of a Breast Cancer Clinical Trial

2004 ◽  
Vol 10 (2) ◽  
pp. 103-120
Author(s):  
Philippe Broët ◽  
Alexander Tsodikov ◽  
Yann De Rycke ◽  
Thierry Moreau
Keyword(s):  
Breast Cancer ◽  
Clinical Trial ◽  
Failure Time ◽  
Cancer Clinical Trial ◽  
Accelerated Failure Time ◽  
Survival Functions ◽  
Accelerated Failure

scholarly journals Alkaline Water and Longevity: A Murine Study

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Massimiliano Magro ◽  
Livio Corain ◽  
Silvia Ferro ◽  
Davide Baratella ◽  
Emanuela Bonaiuto ◽  
...  
Keyword(s):  
Survival Data ◽  
Failure Time ◽  
Control Group ◽  
Accelerated Failure Time ◽  
Survival Functions ◽  
Alkaline Water ◽  
Aft Model ◽  
Accelerated Failure ◽  
Second Year ◽  
Aging Factor

The biological effect of alkaline water consumption is object of controversy. The present paper presents a 3-year survival study on a population of 150 mice, and the data were analyzed with accelerated failure time (AFT) model. Starting from the second year of life, nonparametric survival plots suggest that mice watered with alkaline water showed a better survival than control mice. Interestingly, statistical analysis revealed that alkaline water provides higher longevity in terms of “deceleration aging factor” as it increases the survival functions when compared with control group; namely, animals belonging to the population treated with alkaline water resulted in a longer lifespan. Histological examination of mice kidneys, intestine, heart, liver, and brain revealed that no significant differences emerged among the three groups indicating that no specific pathology resulted correlated with the consumption of alkaline water. These results provide an informative and quantitative summary of survival data as a function of watering with alkaline water of long-lived mouse models.

Bayesian variable selection in the accelerated failure time model with an application to the surveillance, epidemiology, and end results breast cancer data

2016 ◽  
Vol 27 (4) ◽  
pp. 971-990 ◽  
Author(s):  
Zhen Zhang ◽  
Samiran Sinha ◽  
Tapabrata Maiti ◽  
Eva Shipp
Keyword(s):  
Breast Cancer ◽  
Failure Time ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Time Model ◽  
Cancer Data ◽  
Survival Probabilities ◽  
Accelerated Failure ◽  
Failure Time Model ◽  
End Results

Accelerated failure time model is a popular model to analyze censored time-to-event data. Analysis of this model without assuming any parametric distribution for the model error is challenging, and the model complexity is enhanced in the presence of large number of covariates. We developed a nonparametric Bayesian method for regularized estimation of the regression parameters in a flexible accelerated failure time model. The novelties of our method lie in modeling the error distribution of the accelerated failure time nonparametrically, modeling the variance as a function of the mean, and adopting a variable selection technique in modeling the mean. The proposed method allowed for identifying a set of important regression parameters, estimating survival probabilities, and constructing credible intervals of the survival probabilities. We evaluated operating characteristics of the proposed method via simulation studies. Finally, we apply our new comprehensive method to analyze the motivating breast cancer data from the Surveillance, Epidemiology, and End Results Program, and estimate the five-year survival probabilities for women included in the Surveillance, Epidemiology, and End Results database who were diagnosed with breast cancer between 1990 and 2000.

A comparison of all-subset Cox and accelerated failure time models with Cox step-wise regression for node-positive breast cancer

1992 ◽  
Vol 22 (3) ◽  
pp. 263-272 ◽  
Author(s):  
Judy-Anne W. Chapman ◽  
Maureen E. Trudeau ◽  
Kathleen I. Pritchard ◽  
Carol A. Sawka ◽  
Betty G. Mobbs ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Node Positive ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure ◽  
Positive Breast Cancer

scholarly journals Temporally dependent accelerated failure time model for capturing the impact of events that alter survival in disease mapping

Biostatistics ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 666-680 ◽  
Author(s):  
Rachel Carroll ◽  
Andrew B Lawson ◽  
Shanshan Zhao
Keyword(s):  
Breast Cancer ◽  
Failure Time ◽  
Temporal Structure ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Time Model ◽  
Unmeasured Confounding ◽  
Accelerated Failure ◽  
The Impact ◽  
Failure Time Model

Summary The introduction of spatial and temporal frailty parameters in survival models furnishes a way to represent unmeasured confounding in the outcome of interest. Using a Bayesian accelerated failure time model, we are able to flexibly explore a wide range of spatial and temporal options for structuring frailties as well as examine the benefits of using these different structures in certain settings. A setting of particular interest for this work involved using temporal frailties to capture the impact of events of interest on breast cancer survival. Our results suggest that it is important to include these temporal frailties when there is a true temporal structure to the outcome and including them when a true temporal structure is absent does not sacrifice model fit. Additionally, the frailties are able to correctly recover the truth imposed on simulated data without affecting the fixed effect estimates. In the case study involving Louisiana breast cancer-specific mortality, the temporal frailty played an important role in representing the unmeasured confounding related to improvements in knowledge, education, and disease screenings as well as the impacts of Hurricane Katrina and the passing of the Affordable Care Act. In conclusion, the incorporation of temporal, in addition to spatial, frailties in survival analysis can lead to better fitting models and improved inference by representing both spatially and temporally varying unmeasured risk factors and confounding that could impact survival. Specifically, we successfully estimated changes in survival around the time of events of interest.

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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