Abstract
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.
Original language | English (US) |
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Pages (from-to) | 13-25 |
Number of pages | 13 |
Journal | Lifetime Data Analysis |
Volume | 3 |
Issue number | 1 |
DOIs | |
State | Published - 1997 |
Keywords
- Dynamic linear model
- Hierarchical models
- Markov chain Monte Carlo
- Smoothing
- Survival analysis
ASJC Scopus subject areas
- Applied Mathematics