A Flexible Approach to Time-varying Coefficients in the Cox Regression Setting

Daniel J. Sargent

Research output: Contribution to journalArticle

34 Scopus citations

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 languageEnglish (US)
Pages (from-to)13-25
Number of pages13
JournalLifetime Data Analysis
Volume3
Issue number1
DOIs
StatePublished - Jan 1 1997

Keywords

  • Dynamic linear model
  • Hierarchical models
  • Markov chain Monte Carlo
  • Smoothing
  • Survival analysis

ASJC Scopus subject areas

  • Applied Mathematics

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