Accelerated Failure Time (AFT) Model
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An Accelerated Failure Time (AFT) Model is a parametric survival model which assumes that the effect of a covariate is to accelerate or decelerate the life course of a disease by some constant value.
- Context:
- It can be optimized by an AFT System that implements an AFT Model Fitting Algorithm.
- …
- Example(s):
- It may be appropriate when the 'disease' is a result of some mechanical process with a known sequence of intermediary stages.
- …
- Counter-Example(s):
- See: Covariate, Survival Analysis.
References
2020
- (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/Accelerated_failure_time_model Retrieved:2020-2-3.
- 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.
2009
- (Swindell, 2009) ⇒ Swindell, W. R. (2009). Accelerated failure time models provide a useful statistical framework for aging research. Experimental gerontology, 44(3), 190-200. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2718836/
- Survivorship experiments play a central role in aging research and are performed to evaluate whether interventions alter the rate of aging and increase lifespan. The accelerated failure time (AFT) model is seldom used to analyze survivorship data, but offers a potentially useful statistical approach that is based upon the survival curve rather than the hazard function. In this study, AFT models were used to analyze data from 16 survivorship experiments that evaluated the effects of one or more genetic manipulations on mouse lifespan. Most genetic manipulations were found to have a multiplicative effect on survivorship that is independent of age and well-characterized by the AFT model “deceleration factor”. AFT model deceleration factors also provided a more intuitive measure of treatment effect than the hazard ratio, and were robust to departures from modeling assumptions. Age-dependent treatment effects, when present, were investigated using quantile regression modeling. These results provide an informative and quantitative summary of survivorship data associated with currently known long-lived mouse models. In addition, from the standpoint of aging research, these statistical approaches have appealing properties and provide valuable tools for the analysis of survivorship data.
2006
- (Everitt, 2006) ⇒ Brian S. Everitt. (2006). “The Cambridge Dictionary of Statistics. 3rd Edition.” Cambridge University Press. ISBN:0521690277
- QUOTE: Accelerated failure time model: A general model for data consisting of survival times, in which explanatory variables measured on an individual are assumed to act multiplicatively on the time-scale, and so affect the rate at which an individual proceeds along the time axis. Consequently the model can be interpreted in terms of the speed of progression of a disease. In the simplest case of comparing two groups of patients, for example, those receiving treatment A and those receiving treatment B, this model assumes that the survival time of an individual on one treatment is a multiple of the survival time on the other treatment; as a result the probability that an individual on treatment A survives beyond time t is the probability that an individual on treatment B survives beyond time ¢t, where ¢ is an unknown positive constant. When the end-point of interest is the death of a patient, values of ¢one correspond to an acceleration in the time of death of an individual assigned to treatment A, and values of ¢ greater than one indicate the reverse. The parameter ¢ is known as the acceleration factor. [Modelling Survival Data in Medical Research, 2nd edition, 2003, D. Collett, Chapman and Hall/CRC Press, London.]