Semiparametric inference for survival models with step process covariates

The authors consider Bayesian methods for fitting three semiparametric survival models, incorporating time-dependent covariates that are step functions. In particular, these are models due to Cox [Cox (1972) Journal of the Royal Statistical Society, Series B, 347 187-208], Prentice & Kalbfleisch and Cox & Oakes [Cox & Oakes (1984) Analysis of Survival Data, Chapman and Hall, London]. The model due to Prentice & Kalbfleisch [Prentice & Kalbfleisch (1979) Biometrics, 35, 25-39], which has seen very limited use, is given particular consideration. The prior for the baseline distribution in each model is taken to be a mixture of Polya trees and posterior inference is obtained through standard Markov chain Monte Carlo methods. They demonstrate the implementation and comparison of these three models on the celebrated Stanford heart transplant data and the study of the timing of cerebral edema diagnosis during emergency room treatment of diabetic ketoacidosis in children. An important feature of their overall discussion is the comparison of semi-parametric families, and ultimate criterion based selection of a family within the context of a given data set. The Canadian Journal of Statistics 37: 60-79 2009 (C) 2009 Statistical Society of Canada