Results 221 to 230 of about 172,693 (257)

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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Calibration of Proportional Hazards and Accelerated Failure Time Models

Communications in Statistics - Simulation and Computation, 2012
Given a prognostic model based on one population, one may ask: Can this model be used to accurately predict disease in a different population? When the underlying rate of disease differs in the new population, the model must be calibrated. van Houwelingen (2000) considered this calibration problem focusing on proportional hazards models.
Jeannette Simino, M. Hollander, Daniel McGee   +2 more
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Analysis of Failure Time Data with Mixed-Effects Accelerated Failure Time Model

Communications in Statistics - Simulation and Computation, 2011
In randomized clinical trials or observational studies, subjects are recruited at multiple treating sites. Factors that vary across sites may have some influence on outcomes; therefore, they need to be taken into account to get better results. We apply the accelerated failure time (AFT) model with linear mixed effects to analyze failure time data ...
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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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ACCELERATED FAILURE TIME MODELS WITH NONLINEAR COVARIATES EFFECTS

Australian & New Zealand Journal of Statistics, 2007
SummaryAs a flexible alternative to the Cox model, the accelerated failure time (AFT) model assumes that the event time of interest depends on the covariates through a regression function. The AFT model with non‐parametric covariate effects is investigated, when variable selection is desired along with estimation.
Leng, C., Ma, S.
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A semi‐parametric accelerated failure time cure model

Statistics in Medicine, 2002
AbstractA cure model is a useful approach for analysing failure time data in which some subjects could eventually experience, and others never experience, the event of interest. A cure model has two components: incidence which indicates whether the event could eventually occur and latency which denotes when the event will occur given the subject is ...
Chin-Shang, Li, Jeremy M G, Taylor
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The Accelerated Failure Time Model Under Biased Sampling

Biometrics, 2010
Summary Chen (2009, Biometrics) studies the semi‐parametric accelerated failure time model for data that are size biased. Chen considers only the uncensored case and uses hazard‐based estimation methods originally developed for censored observations. However, for uncensored data, a simple linear regression on the log scale is more natural and provides
Micha, Mandel, Ya'akov, Ritov
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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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Linear Interpolation With a Nonparametric Accelerated Failure-Time Model

Journal of the American Statistical Association, 1988
Abstract Assuming a nonparametric accelerated failure-time model, a method is proposed for extrapolating low stress-response probabilities on negative-sloping line segments in the stress-failure-time plane. The method (analogous to linear interpolation in dose-response studies) results in simultaneous extrapolation ahead in time and down in stress.
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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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