Bayesian semiparametric proportional odds models.
Methodology for implementing the proportional odds regression model for survival data assuming a mixture of finite Polya trees (MPT) prior on baseline survival is presented. Extensions to frailties and generalized odds rates are discussed. Although all manner of censoring and truncation can be accommodated, we discuss model implementation, regression diagnostics, and model comparison for right-censored data. An advantage of the MPT model is the relative ease with which predictive densities, survival, and hazard curves are generated. Much discussion is devoted to practical implementation of the proposed models, and a novel MCMC algorithm based on an approximating parametric normal model is developed. A modest simulation study comparing the small sample behavior of the MPT model to a rank-based estimator and a real data example is presented.
Hanson
Timothy
T
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, USA. hanson@biostat.umn.edu
Yang
Mingan
M
D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000145:classification / Q000401:mortality*; D016017:Odds Ratio; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D016019:Survival Analysis