A flexible approach to time-varying coefficients in the Cox regression setting. (English) Zbl 0896.62126

Summary: 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 [D. R. Cox, J. R. Stat. Soc. Ser. B 34, 187-220 (1972; Zbl 0243.62041)]. Typically, it is assumed that a covariate’s effect on the hazard function is constant throughout the course of the study.
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.


62P10 Applications of statistics to biology and medical sciences; meta analysis
62F15 Bayesian inference


Zbl 0243.62041
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