A flexible approach to time-varying coefficients in the Cox regression setting.
Research on methods for studying time-to-event data (survival analysis) has been extensive in recent years. The basic model in use today represents the hazard function for an individual through a proportional hazards model (Cox, 1972). Typically, it is assumed that a covariate's effect on the hazard function is constant throughout the course of the study. In this paper we propose a method to allow for possible deviations from the standard Cox model, by allowing the effect of a covariate to vary over time. This method is based on a dynamic linear model. We present our method in terms of a Bayesian hierarchical model. We fit the model to the data using Markov chain Monte Carlo methods. Finally, we illustrate the approach with several examples.
Sargent
D J
DJ
Mayo Clinic, Cancer Center Statistics, Rochester, MN 55905, USA. sargent.daniel@mayo.edu
D001499:Bayes Theorem; D006801:Humans; D015993:Life Tables; D016014:Linear Models; D008175:Lung Neoplasms / Q000188:drug therapy / Q000401:mortality; D008390:Markov Chains; D009010:Monte Carlo Method; D016016:Proportional Hazards Models; D016019:Survival Analysis