The accelerated failure time model (AFT) is a statistical analysis method used to model the time until an event of interest occurs.  The AFT model finds an association between the response variable with the survival timing. In this case, the response variable is found by taking the logarithm of the survival time, and it contains an error term, that follows a particular probability distribution. 

Uses in Various Fields

It is widely used in reliability engineering and survival analysis to estimate the time it takes for a product or machine to fail. AFT assumes that the logarithm of the survival time is proportional to some linear combination of the predictors. This means that the predictors have a constant effect on the survival time, and that a change in any predictor will result in a proportional change in the survival time. 

There are several advantages to using the AFT model over other survival analysis methods. For one, AFT can handle large datasets and is relatively easy to interpret. Additionally, AFT allows for the direct interpretation of the regression coefficients, as they reflect the log of the hazard ratio (HR) for each predictor. This can be particularly useful when comparing two groups or when interpreting the effect of a given predictor. One common application of the AFT model is in the analysis of clinical trials. By modelling the time until a patient experiences an event of interest (such as disease relapse or death), researchers can gain insights into the effectiveness of a particular treatment or intervention. In addition, AFT can be used to explore the effect of different risk factors on survival outcomes. Overall, the AFT model is a powerful statistical tool that can help researchers better understand the underlying factors that impact the time until an event of interest occurs. With its ability to handle large datasets and provide direct interpretation of regression coefficients, it is a valuable addition to the researcher’s toolkit. 

Assumptions of the Model

One of the important assumptions of the model is that the effect of covariates acts proportionally or multiplicatively as relative to the survival time. Thus, it can be looked upon as 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 the individual proceeds along the time axis. Consequently, the model can be interpreted in terms of the speed of progression of a disease. 

Example

In the simplest case of comparing two groups of patients, for example, those receiving treatment A and those receiving treatment B, this model assume 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 0t, where 0 is an unknown positive constant. When the end point of interest is the death of a patient, values of 0 less than 1 correspond to an acceleration in the time of death of an individual assigned to treatment A, and values of 0 greater than 1 indicate the reverse. The parameter 0 is known as the acceleration factor. This model provides an alternative to the proportional hazard models, assuming that the covariate in the model either accelerates or decelerates the disease life course by a certain constant. This model is generally applied in cases when the disease is the result of a certain mechanical process, with a defined sequence of intermediary stages.