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Flexible parametric accelerated failure time model
Journal of Biopharmaceutical Statistics, 2021Accelerated Failure Time (AFT) models are viable alternatives to the Cox proportional hazard model, where failure times are explicitly modelled with respect to covariates. A major problem with parametric AFT models in practice is that statistical distribution used there often have a limited range of shapes, which may be inadequate to cope with real ...
Steve Su
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Accelerated Failure Time Models For Load Sharing Systems
IEEE Transactions on Reliability, 2014We model the load sharing phenomenon in a k-out-of- m system through the accelerated failure time model. This model leads to multivariate families of distributions for ordered random variables, which are particular cases of the sequential order statistics.
Santosh Sutar, U V Naik-Nimbalkar
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Spline‐based accelerated failure time model
Statistics in Medicine, 2020The accelerated failure time (AFT) model has been suggested as an alternative to the Cox proportional hazards model. However, a parametric AFT model requires the specification of an appropriate distribution for the event time, which is often difficult to identify in real‐life studies and may limit applications.
Menglan Pang +3 more
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A Bayesian Semiparametric Accelerated Failure Time Model
Biometrics, 1999Summary.A Bayesian semiparametric approach is described for an accelerated failure time model. The error distribution is assigned a Polya tree prior and the regression parameters a noninformative hierarchical prior. Two cases are considered: the first assumes error terms are exchangeable; the second assumes that error terms are partially exchangeable ...
Walker, Stephen, Mallick, Bani K.
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A homoscedasticity test for the accelerated failure time model
Computational Statistics, 2018The semiparametric accelerated failure time (AFT) model is a popular linear model in survival analysis. AFT model and its associated inference methods assume homoscedasticity of the survival data. It is shown that violation of this assumption will lead to inefficient parameter estimation and anti-conservative confidence interval estimation, and thus ...
Lili Yu, Liang Liu, Ding-Geng Chen
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Modelling Accelerated Failure Time with a Dirichlet Process
Biometrika, 1988The relationship between survival times \(T=(T_ 1,...,T_ n)\) and covariates \(x_ i=(1,x_{i1},...,x_{ip})\) is modelled via the accelerated failure time model \(T_ i=\exp (-x_ i\beta)V_ i,\) where \(\beta\) is a vector of fixed unknown regression coefficients, and \(V\equiv (V_ 1,...,V_ n)\) is a random sample of size n from some distribution P.
Christensen, Ronald, Johnson, Wesley
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Using Frailties in the Accelerated Failure Time Model
Lifetime Data Analysis, 2001The accelerated failure time (AFT) model is an important alternative to the Cox proportional hazards model (PHM) in survival analysis. For multivariate failure time data we propose to use frailties to explicitly account for possible correlations (and heterogeneity) among failure times. An EM-like algorithm analogous to that in the frailty model for the
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Accelerated failure time modeling via nonparametric mixtures
Biometrics, 2021AbstractAn accelerated failure time (AFT) model assuming a log‐linear relationship between failure time and a set of covariates can be either parametric or semiparametric, depending on the distributional assumption for the error term. Both classes of AFT models have been popular in the analysis of censored failure time data.
Byungtae Seo, Sangwook Kang
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Robust estimation in accelerated failure time models
Lifetime Data Analysis, 2018The accelerated failure time model is widely used for analyzing censored survival times often observed in clinical studies. It is well-known that the ordinary maximum likelihood estimators of the parameters in the accelerated failure time model are generally sensitive to potential outliers or small deviations from the underlying distributional ...
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ℓ0-Regularized high-dimensional accelerated failure time model
Computational Statistics & Data Analysis, 2022zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Chao Cheng +4 more
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