The estimation of optimum cut points for covariates in lifetime regression models is of great interest under a medical view. Usually the choice of covariate cut points is made in an arbitrary way following the clinical expert knowledge. In this paper, it is proposed a simple and practical Bayesian approach which could be used to different lifetime distributions under AFT (accelerated failure time) modeling approach assuming censored or uncensored data to get optimum cut points with larger prognostic effects. For the Bayesian approach, MCMC simulations are used to get estimation for the cut points under a squared error loss (SEL) function. The proposed methodology is illustrated with three medical lifetime data sets.
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