scholarly journals Accelerated failure time models for censored survival data under referral bias

Biostatistics ◽  
2012 ◽  
Vol 14 (2) ◽  
pp. 313-326 ◽  
Author(s):  
H. Wang ◽  
H. Dai ◽  
B. Fu
Keyword(s):  
Survival Data ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Censored Survival Data ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure ◽  
Referral Bias

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.

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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