Semiparametric Bayesian modelling of stratified survival data using mixtures
by
Renate Meyer
University of Auckland
Coauthors: Bo Cai (University of Auckland)

A stratified proportional hazards model is commonly used to analyse survival data collected over many strata, for example in multicentre clinical trials. Frailty models can be regarded as a compromise between a stratified and an unstratified analysis. Instead of including frailties, i.e. iid random variables for each stratum, in this paper we consider treating the whole stratum-specific baseline hazard function as random as in Carlin and Hodges (1999). We are using a Bayesian nonparametric approach to estimate the baseline hazards using mixtures of triangular distributions (Perron and Mengersen, 2001). The number of mixands is an unknown parameter, and estimated simultaneously with other parameters using a reversible jump Markov chain Monte Carlo algorithm. We illustrate the technique using clinical trial data and compare results to parametric alternatives and a semiparametric alternative using mixtures of beta distributions (Gelfand and Mallick, 1995).

Date received: April 4, 2002