Flexible Bayesian Modelling for Survival Data

Author: Gustafson P.

Source: Lifetime Data Analysis, Volume 4, Number 3, August 1998 , pp. 281-299(19)

Publisher: Springer

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Abstract:

The analysis of failure time data often involves two strong assumptions. The proportional hazards assumption postulates that hazard rates corresponding to different levels of explanatory variables are proportional. The additive effects assumption specifies that the effect associated with a particular explanatory variable does not depend on the levels of other explanatory variables. A hierarchical Bayes model is presented, under which both assumptions are relaxed. In particular, time-dependent covariate effects are explicitly modelled, and the additivity of effects is relaxed through the use of a modified neural network structure. The hierarchical nature of the model is useful in that it parsimoniously penalizes violations of the two assumptions, with the strength of the penalty being determined by the data.

Keywords: hierarchical Bayes; Markov chain Monte Carlo; survival analysis

Language: English

Document Type: Regular paper

Affiliations: 1: Department of Statistics, University of British Columbia, Vancouver, B.C., Canada V6T 1Z2

Publication date: 1998-08-01