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

Daniel J. Sargent

Research output: Contribution to journalArticle

34 Citations (Scopus)

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
StatePublished - 1997

Fingerprint

Cox Regression
Time-varying Coefficients
Hazard Function
Proportional Hazards Models
Covariates
Hazards
Dynamic Linear Models
Bayesian Hierarchical Model
Monte Carlo Method
Markov Chains
Cox Proportional Hazards Model
Cox Model
Survival Analysis
Markov Chain Monte Carlo Methods
Standard Model
Linear Models
Deviation
Vary
Markov processes
Monte Carlo methods

Keywords

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

ASJC Scopus subject areas

  • Applied Mathematics
  • Medicine(all)

Cite this

A Flexible Approach to Time-varying Coefficients in the Cox Regression Setting. / Sargent, Daniel J.

In: Lifetime Data Analysis, Vol. 3, No. 1, 1997, p. 13-25.

Research output: Contribution to journalArticle

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