title: A simple approach to fitting Bayesian survival models.
abstract: There has been much recent work on Bayesian approaches to survival analysis, incorporating features such as flexible baseline hazards, time-dependent covariate effects, and random effects. Some of the proposed methods are quite complicated to implement, and we argue that as good or better results can be obtained via simpler methods. In particular, the normal approximation to the log-gamma distribution yields easy and efficient computational methods in the face of simple multivariate normal priors for baseline log-hazards and time-dependent covariate effects. While the basic method applies to piecewise-constant hazards and covariate effects, it is easy to apply importance sampling to consider smoother functions.
authors.affiliation: Department of Statistics, University of British Columbia, Vancouver, BC, Canada. gustaf@stat.ubc.ca
authors.forename: Paul
authors.initials: P
authors.lastname: Gustafson
authors.forename: Dana
authors.initials: D
authors.lastname: Aeschliman
authors.forename: Adrian R
authors.initials: AR
authors.lastname: Levy
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D012680:Sensitivity and Specificity; D016019:Survival Analysis