Bayesian Analysis Using the LIFEREG Procedure

The LIFEREG procedure fits parametric models to failure-time data that can be uncensored, right-censored, left-censored, or interval-censored. The models for the response variable consist of a linear effect (which is composed of the covariates) and a random disturbance term. The distribution of the random disturbance can be taken from a class of distributions that includes the extreme value, normal, logistic, and, by using a log transformation, the exponential, Weibull, lognormal, log-logistic, and three-parameter gamma distributions. PROC LIFEREG uses either frequentist or Bayesian methods to fit models.

To perform Bayesian analyses with PROC LIFEREG, you specify a model essentially the same way you do for a frequentist approach, but you add a BAYES statement to request Bayesian estimation methods for fitting the model. The BAYES statement requests that the parameters of the model be estimated by Markov chain Monte Carlo sampling techniques and provides options that enable you to specify prior information, control the sampling, and obtain posterior summary statistics and convergence diagnostics. You can also save the posterior samples to a SAS data set for further analysis.

Sampling Algorithms

The LIFEREG procedure supports the following sampling algorithms:


The LIFEREG procedure supports the following priors:

Parameter Prior
Regression coefficients Normal, uniform
Exponential scale Gamma, improper
Location-scale model scale parameter Gamma
Weibull scale Gamma
Three-parameter gamma shape Normal
Weibull shape Gamma

Bayesian Analysis Examples