Accelerated Failure Time Models
Accelerated Failure Time (AFT) models are a class of statistical models used in survival analysis to analyze time-to-event data, such as failure times in engineering or survival times in medical studies. They assume that the effect of covariates (e.g., treatment, age) is to accelerate or decelerate the time to an event, with a multiplicative relationship on the time scale. These models are commonly applied in fields like reliability engineering, biostatistics, and economics to predict failure or survival times based on explanatory variables.
Developers should learn AFT models when working on projects involving predictive analytics for time-to-event outcomes, such as estimating equipment failure in IoT systems, patient survival in healthcare applications, or customer churn in business analytics. They are particularly useful in scenarios where the proportional hazards assumption of Cox models does not hold, offering a direct interpretation of how covariates affect the time scale, which can be more intuitive for stakeholders. For example, in software reliability, AFT models can help predict system downtime based on usage patterns or environmental factors.