Bayesian model selection: analysis of a survival model with a surviving fraction.
We describe a methodology for model comparison in a Bayesian framework as applied to survival with a surviving fraction. This is illustrated using a case study of a randomized and controlled clinical trial investigating time until recurrence of depression. Posterior distributions are simulated using Metropolis-within-Gibbs Markov chain methods. Models reflecting the effects of covariates on the log odds of being in the surviving fraction, the log of the hazard rate, as well as both and neither are compared. Bayes factors for comparing the models are obtained by using the bridge sampling method of calculating normalizing constants.
Seltman
H
H
Department of Statistics, Carnegie Mellon University, 232 Baker Hall, Pittsburgh, PA 15213-3890, U.S.A. hseltman@stat.cmu.edu
Greenhouse
J
J
Wasserman
L
L
D000465:Algorithms; D000929:Antidepressive Agents, Tricyclic / Q000627:therapeutic use; D001499:Bayes Theorem; D003198:Computer Simulation; D003863:Depression / Q000188:drug therapy / Q000517:prevention & control; D018572:Disease-Free Survival; D006801:Humans; D007099:Imipramine / Q000627:therapeutic use; D008390:Markov Chains; D008954:Models, Biological; D015337:Multicenter Studies as Topic / Q000379:methods; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D016019:Survival Analysis
D000929:Antidepressive Agents, Tricyclic; D007099:Imipramine