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accelerated failure time model : ウィキペディア英語版
accelerated failure time model
In the statistical area of survival analysis, an accelerated failure time model (AFT model) is a parametric model that provides an alternative to the commonly used proportional hazards models. Whereas a proportional hazards model assumes that the effect of a covariate is to multiply the hazard by some constant, an AFT model assumes that the effect of a covariate is to accelerate or decelerate the life course of a disease by some constant. This is especially appealing in a technical context where the 'disease' is a result of some mechanical process with a known sequence of intermediary stages.
==Model specification==
In full generality, the accelerated failure time model can be specified as
::
\lambda(t|\theta)=\theta\lambda_0(\theta t)

where \theta denotes the joint effect of covariates, typically \theta=\exp(-(+ \cdots + \beta_pX_p )). (Specifying the regression coefficients with a negative sign implies that high values of the covariates ''increase'' the survival time, but this is merely a sign convention; without a negative sign, they increase the hazard.)
This is satisfied, if the probability density function of the event is taken to be f(t|\theta)=\theta f_0(\theta t), from which is follows for the survival function that S(t|\theta)=S_0(\theta t). From this it is easy to see that the moderated life time T is distributed such that T\theta and the unmoderated life time T_0 have the same distribution. Consequently, log(T) can be written as
::
log(T)=-log(\theta)+log(T\theta):=-log(\theta)+\epsilon

where the last term is distributed as log(T_0), i.e. independently of \theta. This reduces the accelerated failure time model into regression analysis (typically a linear model) where -log(\theta) represents the fixed effects, and \epsilon represents the noise. Different distributional forms of \epsilon imply different distributional forms of T_0, i.e. different baseline distributions of the survival time. It is typical of survival-analytic contexts, that many of the observations are censored, i.e. we only know that T_i>t_i, not T_i=t_i. In fact, the former case represents survival, while the later case represents an event/death/censoring during the follow-up. These right-censored observations can pose technical challenges for estimating the model, if the distribution of T_0 is unusual.
The interpretation of \theta in accelerated failure time models is straight forward: E.g. \theta=2 means that everything in the relevant life history of an individual happens twice as fast. For example, if the model concerns the development of a tumor, it means that all of the pre-stages progress twice as fast as for the unexposed individual, implying that the expected time until a clinical disease is 0.5 of the baseline time. However, this does not mean that the hazard function \lambda(t|\theta) is always twice as high - that would be the proportional hazards model.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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