Accelerated Failure Model And Its Application In Medical Research

In recent years, the theory of Survival Analysis has been widely used in many fields of human life, and it can be used to offer scientific guidance to human health care and industrial producing process. In the scope oC medical research, interests of biological statistist is focused on how to select the significant factor that can influence the occurrence and proceeding of some disease. There are two most popular models in this area: proportional hazards model(Cox model, PHD model) and the accelerated failure time model (AFT model), they have their own advantage, disadvantage and the proper situation respectively. A great deal of valuable results have been obtained on Cox proportional hazards model. It is regretful that the proceeding on accelerated failure time model is not as successful as on Cox one, and it is seldom being used in medical research. However, it is a kind of model which has its importance and can be used to analysis survival data, particularly in situations that Cox model fails to work.In this article, we discuss the problem that how to analysis survival data with the AFT model. Four parametric models are used in our discuss, they are Weibull regression model, log logistic regression model, log normal regression model and generalized gamma regression model. We also fit the same survival data with simulated annealing method and resampling method semiparametricly.Although the classical Cox model may be not appropriate, the accelerated failure time model can fit survival data well enough. It should be regarded as an alternative to the Cox proportional hazards model in medical research.