title: Bayesian accelerated failure time analysis with application to veterinary epidemiology.
abstract: Standard methods for analysing survival data with covariates rely on asymptotic inferences. Bayesian methods can be performed using simple computations and are applicable for any sample size. We propose a practical method for making prior specifications and discuss a complete Bayesian analysis for parametric accelerated failure time regression models. We emphasize inferences for the survival curve rather than regression coefficients. A key feature of the Bayesian framework is that model comparisons for various choices of baseline distribution are easily handled by the calculation of Bayes factors. Such comparisons between non-nested models are difficult in the frequentist setting. We illustrate diagnostic tools and examine the sensitivity of the Bayesian methods.
authors.affiliation: Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico 87131, USA.
authors.forename: E J
authors.initials: EJ
authors.lastname: Bedrick
authors.forename: R
authors.initials: R
authors.lastname: Christensen
authors.forename: W O
authors.initials: WO
authors.lastname: Johnson
mesh_terms: D000034:Abortion, Veterinary / Q000453:epidemiology*; D000818:Animals; D001499:Bayes Theorem; D002417:Cattle; D005260:Female; D011247:Pregnancy; D012044:Regression Analysis; D012307:Risk Factors; D018401:Sample Size