scholarly journals Accelerated Failure Time Survival Model to Analyze Morris Water Maze Latency Data

2020 ◽  
Author(s):  
Clark R. Andersen ◽  
Jordan Wolf ◽  
Kristofer Jennings ◽  
Donald S. Prough ◽  
Bridget E. Hawkins
Keyword(s):  
Morris Water Maze ◽  
Water Maze ◽  
Failure Time ◽  
Survival Model ◽  
Accelerated Failure Time ◽  
Latency Data ◽  
Accelerated Failure

scholarly journals Generalized Topp-Leone-Weibull AFT Modelling: A Bayesian Analysis with MCMC Tools using R and Stan

2021 ◽  
Vol 50 (5) ◽  
pp. 52-76
Author(s):  
Md. Ashraf-Ul-Alam ◽  
Athar Ali Khan
Keyword(s):  
Weibull Distribution ◽  
Survival Data ◽  
Failure Time ◽  
Survival Model ◽  
Accelerated Failure Time ◽  
Baseline Model ◽  
Censored Survival Data ◽  
Aft Model ◽  
Different Types ◽  
Accelerated Failure

The generalized Topp-Leone-Weibull (GTL-W) distribution is a generalization of Weibull distribution which is obtained by using generalized Topp-Leone (GTL) distribution as a generator and considering Weibull distribution as a baseline distribution. Weibull distribution is a widely used survival model that has monotone- increasing or decreasing hazard. But it cannot accommodate bathtub shaped and unimodal shaped hazards. As a survival model, GTL-W distribution is more flexible than the Weibull distribution to accommodate different types of hazards. The present study aims at fitting GTL-W model as an accelerated failure time (AFT) model to censored survival data under Bayesian setting using R and Stan languages. The GTL-W AFT model is compared with its sub-model and the baseline model. The Bayesian model selection criteria LOOIC and WAIC are applied to select the best model.

scholarly journals Bayesian accelerated failure time model for space-time dependency in a geographically augmented survival model

2015 ◽  
Vol 26 (5) ◽  
pp. 2244-2256 ◽  
Author(s):  
Georgiana Onicescu ◽  
Andrew Lawson ◽  
Jiajia Zhang ◽  
Mulugeta Gebregziabher ◽  
Kristin Wallace ◽  
...  
Keyword(s):  
Failure Time ◽  
Space Time ◽  
Survival Model ◽  
Accelerated Failure Time Model ◽  
Accelerated Failure Time ◽  
Time Dependency ◽  
Time Model ◽  
Accelerated Failure ◽  
Failure Time Model

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.

scholarly journals NONPARAMETRIC IDENTIFICATION OF ACCELERATED FAILURE TIME COMPETING RISKS MODELS

2013 ◽  
Vol 29 (5) ◽  
pp. 905-919 ◽  
Author(s):  
Sokbae Lee ◽  
Arthur Lewbel
Keyword(s):  
Competing Risks ◽  
Failure Time ◽  
Nonparametric Identification ◽  
Accelerated Failure Time ◽  
Rank Condition ◽  
Survivor Function ◽  
Auction Models ◽  
Regression Functions ◽  
Accelerated Failure ◽  
Identification Strategy

We provide new conditions for identification of accelerated failure time competing risks models. These include Roy models and some auction models. In our setup, unknown regression functions and the joint survivor function of latent disturbance terms are all nonparametric. We show that this model is identified given covariates that are independent of latent errors, provided that a certain rank condition is satisfied. We present a simple example in which our rank condition for identification is verified. Our identification strategy does not depend on identification at infinity or near zero, and it does not require exclusion assumptions. Given our identification, we show estimation can be accomplished using sieves.

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