The LIFEREG Procedure

Consider the data on melanoma patients from a clinical trial described in Ibrahim, Chen, and Sinha (2001). A partial listing of the data is shown in Output 57.7.1.

The survival time is modeled by a Weibull regression model with three covariates. An analysis of the right-censored survival data is performed with PROC LIFEREG to obtain Bayesian estimates of the regression coefficients by using the following SAS statements:

ods graphics on;
proc lifereg data=e1684;
   class Sex;
   model Survtime*Survcens(1)=Age Sex Perform / dist=Weibull;
   bayes WeibullShapePrior=gamma seed=9999;
run;
ods graphics off;

Maximum likelihood estimates of the model parameters shown in Output 57.7.2 are displayed by default.

Since no prior distributions for the regression coefficients were specified, the default uniform improper distributions shown in the "Uniform Prior for Regression Coefficients" table in Output 57.7.3 are used. The specified gamma prior for the Weibull shape parameter is also shown in Output 57.7.3.

Fit statistics, descriptive statistics, interval statistics, and the sample parameter correlation matrix for the posterior sample are displayed in the tables in Output 57.7.4. Since noninformative prior distributions for the regression coefficients were used, the mean and standard deviations of the posterior distributions for the model parameters are close to the maximum likelihood estimates and standard errors.

The default diagnostic statistics are displayed in Output 57.7.5. See the section Assessing Markov Chain Convergence in Chapter 7: Introduction to Bayesian Analysis Procedures, for more details on Bayesian convergence diagnostics.

Trace, autocorrelation, and density plots for the seven model parameters are shown in Output 57.7.6 through Output 57.7.10. These plots show no indication that the Markov chains have not converged. See the sections Assessing Markov Chain Convergence and Visual Analysis via Trace Plots in Chapter 7: Introduction to Bayesian Analysis Procedures, for more information about assessing the convergence of the chain of posterior samples.

Output 57.7.6: Diagnostic Plots

Output 57.7.7: Diagnostic Plots

Output 57.7.8: Diagnostic Plots

Output 57.7.9: Diagnostic Plots

Output 57.7.10: Diagnostic Plots