# Accelerated Failure Time Model

An Accelerated Failure Time Model is an alternative parametric model to the proportional hazards model.

**AKA:**AFT Model, AFT.**See:**Statistics, Covariate, Survival Analysis, Proportional Hazards Model

## References

### 2015

- (Wikipedia, 2015) ⇒ http://www.wikiwand.com/en/Accelerated_failure_time_model Retrieved 2017-07-24
- 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

- In the statistical area of survival analysis, an

### 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.