Results 241 to 250 of about 519,139 (282)

Using Frailties in the Accelerated Failure Time Model

Lifetime Data Analysis, 2001
The 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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Modelling Accelerated Failure Time with a Dirichlet Process

Biometrika, 1988
The 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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Accelerated failure time models for counting processes

Biometrika, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lin, D. Y., Wei, L. J., Ying, Zhiliang
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Accelerated failure time model with quantile information

Annals of the Institute of Statistical Mathematics, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhao, Mu, Wang, Yixin, Zhou, Yong
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Rank-based inference for the accelerated failure time model

Biometrika, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jin, Zhezhen, Lin, D. Y., Wei, L. J., Ying, Zhiliang   +3 more
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Nonparametric Analysis of an Accelerated Failure Time Model

Biometrika, 1981
SUMMARY Survival distributions can be characterized by and compared through their hazard functions. Tests using a proportional hazards model have good power if the two hazards do not cross, but without time-dependent covariates can have low power if they do.
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Bayesian semiparametric inference for the accelerated failure‐time model

Canadian Journal of Statistics, 1997
AbstractBayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture‐of‐Dirichlet‐processes
Kuo, Lynn, Mallick, Bani
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Accelerated Failure Time Models with Auxiliary Covariates

Journal of Biometrics & Biostatistics, 2012
In this paper we study semi-parametric inference procedure for accelerated failure time models with auxiliary information about a main exposure variable. We use a kernel smoothing method to introduce the auxiliary covariate to the likelihood function. The regression parameters are then estimated through maximization of the estimated likelihood function.
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Semiparametric Accelerated Failure Time Modeling for Clustered Failure Times From Stratified Sampling

Journal of the American Statistical Association, 2015
Clustered failure times often arise from studies with stratified sampling designs where it is desired to reduce both cost and sampling error. Semiparametric accelerated failure time (AFT) models have not been used as frequently as Cox relative risk models in such settings due to lack of efficient and reliable computing routines for inferences.
Sy Han Chiou, Sangwook Kang, Jun Yan
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Accelerated Failure Time Models

2023
Ton J. Cleophas, Aeilko H. Zwinderman
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