In this paper, I extend the proposed model by McKeague and Tighiouart (2000) to handle time-varying correlated covariate effects for the analysis of survival data. I use the conditional predictive ordinates (CPO’s) for model comparison and the methodology is illustrated by an application to nasopharynx cancer survival data. A reversible jump MCMC sampler to estimate the CPO’s will be presented.
"Modeling Correlated Time-Varying Covariate Effects In A Cox-Type Regression Model,"
Journal of Modern Applied Statistical Methods:
1, Article 14.
Available at: http://digitalcommons.wayne.edu/jmasm/vol2/iss1/14