Risk patterns in drug safety study using relative times by accelerated failure time models when proportional hazards assumption is questionable: an illustrative case study of cancer risk of patients on glucose-lowering therapies

2015 ◽  
Vol 14 (5) ◽  
pp. 382-394 ◽  
Author(s):  
Edmond S.-W. Ng ◽  
Olaf H. Klungel ◽  
Rolf H. H. Groenwold ◽  
Tjeerd-Pieter van Staa
Keyword(s):  
Proportional Hazards ◽  
Failure Time ◽  
Accelerated Failure Time ◽  
Illustrative Case ◽  
Safety Study ◽  
Glucose Lowering ◽  
Accelerated Failure Time Models ◽  
Accelerated Failure ◽  
Illustrative Case Study

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.

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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